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Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv:1707.01836. 2017."},{"key":"ref15","series-title":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","first-page":"1","article-title":"ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features","author":"Salem","year":"2018 Oct 17\u201319"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41746-018-0070-0","article-title":"Natural language generation for electronic health records","volume":"1","author":"Lee","year":"2018","journal-title":"npj Digit Med"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s12938-018-0496-2","article-title":"Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder","volume":"17","author":"Wang","year":"2018","journal-title":"Biomed Eng 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challenges for protecting health information in a new era","volume":"22","author":"Murdoch","year":"2021","journal-title":"BMC Med Ethics"},{"key":"ref22","series-title":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","first-page":"1","article-title":"Performance analysis and CPU vs GPU comparison for deep learning","author":"Buber","year":"2018 Oct 25\u201327"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1186\/s13063-021-05489-x","article-title":"The role of machine learning in clinical research: transforming the future of evidence generation","volume":"22","author":"Weissler","year":"2021","journal-title":"Trials"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J Big 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Meas"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"961724","DOI":"10.3389\/fphys.2022.961724","article-title":"Abnormal ECG detection based on an adversarial autoencoder","volume":"13","author":"Shan","year":"2022","journal-title":"Front Physiol"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"80510","DOI":"10.1155\/2007\/80510","article-title":"Automatic seizure detection based on time-frequency analysis and artificial neural networks","volume":"2007","author":"Tzallas","year":"2007","journal-title":"Comput Intell Neurosci"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compbiomed.2018.03.016","article-title":"A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification","volume":"96","author":"Yildirim","year":"2018","journal-title":"Comput Biol Med"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans Inf Theory"},{"key":"ref32","series-title":"Proceedings of 3rd International Conference on Document Analysis and Recognition","first-page":"278","article-title":"Random decision forests","author":"Ho","year":"1995 Aug 14\u201316"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann Statist"},{"key":"ref34","first-page":"482","article-title":"Machine un-learning: an overview of techniques, applications, and future directions","volume":"16","author":"Sai","year":"2024","journal-title":"Cogn Comput"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"New Engl J Med"},{"key":"ref36","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4842-2845-6","author":"Kim","year":"2017","journal-title":"Matlab deep learning: with machine learning, neural networks and artificial intelligence"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s13735-021-00218-1","article-title":"A review on deep learning in medical image analysis","volume":"11","author":"Suganyadevi","year":"2022","journal-title":"Int J Multimed Inf Retr"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: general overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J Radiol"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","article-title":"Deep learning for health informatics","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Sci Rep"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: review, opportunities and challenges","volume":"19","author":"Miotto","year":"2018","journal-title":"Brief Bioinform"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"1273253","DOI":"10.3389\/fpubh.2023.1273253","article-title":"Medical image analysis using deep learning algorithms","volume":"11","author":"Li","year":"2023","journal-title":"Front Public Health"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1093\/europace\/euaa377","article-title":"Deep learning and the electrocardiogram: review of the current state-of-the-art","volume":"23","author":"Somani","year":"2021","journal-title":"Europace"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"14681","DOI":"10.1007\/s00521-021-06352-5","article-title":"Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review","volume":"35","author":"Altaheri","year":"2023","journal-title":"Neural Comput Appl"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","article-title":"Machine learning and data mining methods in diabetes research","volume":"15","author":"Kavakiotis","year":"2017","journal-title":"Comput Struct Biotechnol J"},{"key":"ref49","first-page":"149","author":"Choubey","year":"2021","journal-title":"Machine learning methods for signal, image and speech processing"},{"key":"ref50","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-0716-1418-1","author":"James","year":"2021","journal-title":"An introduction to statistical learning"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s13347-023-00635-6","article-title":"On the philosophy of unsupervised learning","volume":"36","author":"Watson","year":"2023","journal-title":"Philos Technol"},{"key":"ref52","unstructured":"Weber T, Racani\u00e8re S, Reichert DP, Buesing L, Guez A, Rezende DJ, et al. Imagination-augmented agents for deep reinforcement learning. arXiv:1707.06203. 2017."},{"key":"ref53","doi-asserted-by":"crossref","first-page":"E969","DOI":"10.3390\/s20040969","article-title":"Deep learning in physiological signal data: a survey","volume":"20","author":"Rim","year":"2020","journal-title":"Sensors"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1055\/s-0038-1667083","article-title":"Deep learning on 1-D biosignals: a taxonomy-based survey","volume":"27","author":"Ganapathy","year":"2018","journal-title":"Yearb Med Inform"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: a review","volume":"16","author":"Craik","year":"2019","journal-title":"J Neural Eng"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1021\/acs.molpharmaceut.5b00982","article-title":"Applications of deep learning in biomedicine","volume":"13","author":"Mamoshina","year":"2016","journal-title":"Mol Pharm"},{"key":"ref57","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief Bioinform"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/TNNLS.2018.2790388","article-title":"Applications of deep learning and reinforcement learning to biological data","volume":"29","author":"Mahmud","year":"2018","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1007\/s12652-023-04617-6","article-title":"Deep learning methods for biomedical information analysis","volume":"14","author":"Zhang","year":"2023","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"ref60","doi-asserted-by":"crossref","first-page":"e38454","DOI":"10.2196\/38454","article-title":"State-of-the-art deep learning methods on electrocardiogram data: systematic review","volume":"10","author":"Petmezas","year":"2022","journal-title":"JMIR Med Inform"},{"key":"ref61","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.3390\/diagnostics12123067","article-title":"Using recurrent neural networks for predicting type-2 diabetes from genomic and tabular data","volume":"12","author":"Srinivasu","year":"2022","journal-title":"Diagnostics"},{"key":"ref62","doi-asserted-by":"crossref","first-page":"4050","DOI":"10.3390\/app9194050","article-title":"A review of deep learning based speech synthesis","volume":"9","author":"Ning","year":"2019","journal-title":"Appl Sci"},{"key":"ref63","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"ref64","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s12984-023-01169-w","article-title":"Generative adversarial networks in EEG analysis: an overview","volume":"20","author":"Habashi","year":"2023","journal-title":"J Neuroeng Rehabil"},{"key":"ref65","series-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"3619","article-title":"EMG data augmentation for grasp classification using generative adversarial networks","author":"Mendez","year":"2022 Jul 11\u201315"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/978-3-030-70296-0_54","author":"Sun","year":"2021","journal-title":"Advances in artificial intelligence and applied cognitive computing"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"35592","DOI":"10.1109\/ACCESS.2020.2974712","article-title":"Generalization of convolutional neural networks for ECG classification using generative adversarial networks","volume":"8","author":"Shaker","year":"2020","journal-title":"IEEE Access"},{"key":"ref68","series-title":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"131","article-title":"CNN architectures for large-scale audio classification","author":"Hershey","year":"2017 Mar 5\u20139"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"569050","DOI":"10.3389\/fphys.2020.569050","article-title":"Deep learning algorithm classifies heartbeat events based on electrocardiogram signals","volume":"11","author":"Liang","year":"2020","journal-title":"Front Physiol"},{"key":"ref70","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1186\/s12911-020-01199-7","article-title":"Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis","volume":"20","author":"Shabaan","year":"2020","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref71","series-title":"2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)","first-page":"994","article-title":"IoT healthcare analytics: the importance of anomaly detection","author":"Ukil","year":"2016 Mar 23\u201325"},{"key":"ref72","doi-asserted-by":"crossref","first-page":"325","DOI":"10.3389\/fphys.2018.00325","article-title":"Automatic change detection for real-time monitoring of EEG signals","volume":"9","author":"Gao","year":"2018","journal-title":"Front Physiol"},{"key":"ref73","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.bspc.2016.09.010","article-title":"Single channel EEG analysis for detection of depression","volume":"31","author":"Bachmann","year":"2017","journal-title":"Biomed Signal Process Control"},{"key":"ref74","series-title":"2010 Annual International Conference of the IEEE Engineering in Medicine and Biology","first-page":"4658","article-title":"Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds","author":"Devuyst","year":"2010 Aug 31\u2013Sep 4"},{"key":"ref75","doi-asserted-by":"crossref","first-page":"6925107","DOI":"10.1155\/2020\/6925107","article-title":"Anomaly detection in EEG signals: a case study on similarity measure","volume":"2020","author":"Chen","year":"2020","journal-title":"Comput Intell Neurosci"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.knosys.2017.05.005","article-title":"A decision support system for automated identification of sleep stages from single-channel EEG signals","volume":"128","author":"Hassan","year":"2017","journal-title":"Knowl Based Syst"},{"key":"ref77","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s13721-020-00272-5","article-title":"Classification of heart sound short records using bispectrum analysis approach images and deep learning","volume":"9","author":"Alqudah","year":"2020","journal-title":"Netw Model Anal Health Inform Bioinform"},{"key":"ref78","doi-asserted-by":"crossref","first-page":"4877","DOI":"10.1007\/s12652-021-03247-0","article-title":"ECG heartbeatarrhythmias classification: a comparison study between different types of spectrum representation and convolutional neural networks architectures","volume":"13","author":"Alqudah","year":"2022","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"ref79","doi-asserted-by":"crossref","first-page":"e221","DOI":"10.1097\/CCM.0000000000005171","article-title":"Pathophysiologic signature of impending ICU hypoglycemia in bedside monitoring and electronic health record data: model development and external validation","volume":"50","author":"Horton","year":"2022","journal-title":"Crit Care Med"},{"key":"ref80","doi-asserted-by":"crossref","first-page":"E6460","DOI":"10.3390\/s20226460","article-title":"Developing an individual glucose prediction model using recurrent neural network","volume":"20","author":"Kim","year":"2020","journal-title":"Sensors"},{"key":"ref81","series-title":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"2605","article-title":"Deep convolutional autoencoder for EEG noise filtering","author":"Leite","year":"2018 Dec 3\u20136"},{"key":"ref82","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.1088\/0967-3334\/37\/12\/2214","article-title":"A stacked contractive denoising auto-encoder for ECG signal denoising","volume":"37","author":"Xiong","year":"2016","journal-title":"Physiol Meas"},{"key":"ref83","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.cmpb.2015.08.004","article-title":"Multi-modality sparse representation-based classification for Alzheimer\u2019s disease and mild cognitive impairment","volume":"122","author":"Xu","year":"2015","journal-title":"Comput Methods Programs Biomed"},{"key":"ref84","doi-asserted-by":"crossref","first-page":"124004","DOI":"10.1088\/1361-6579\/ac9e8a","article-title":"Multi-modal fusion model for predicting adverse cardiovascular outcome post percutaneous coronary intervention","volume":"43","author":"Bhattacharya","year":"2022","journal-title":"Physiol Meas"},{"key":"ref85","series-title":"Proceedings of the 2nd International Conference on Digital Tools & Uses Congress","first-page":"1","article-title":"A multimodal biometric identification system based on ECG and PPG signals","author":"Yaacoubi","year":"2020"},{"key":"ref86","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41746-020-00320-4","article-title":"Multi-task deep learning for cardiac rhythm detection in wearable devices","volume":"3","author":"Torres-Soto","year":"2020","journal-title":"npj Digit Med"},{"key":"ref87","unstructured":"Wallach I, Dzamba M, Heifets A. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv:1510.02855. 2015."},{"key":"ref88","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1021\/acs.jcim.0c00971","article-title":"OpenChem: a deep learning toolkit for computational chemistry and drug design","volume":"61","author":"Korshunova","year":"2021","journal-title":"J Chem Inf Model"},{"key":"ref89","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1007\/978-3-030-60245-1_20","volume":"12452","author":"Zhan","year":"2020","journal-title":"Algorithms and architectures for parallel processing"},{"key":"ref90","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1186\/s13063-021-05546-5","article-title":"Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial","volume":"22","author":"Wilson","year":"2021","journal-title":"Trials"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","article-title":"Deepr: a convolutional net for medical records","volume":"21","author":"Nguyen","year":"2017","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref92","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"Harutyunyan","year":"2019","journal-title":"Sci Data"},{"key":"ref93","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s41666-019-00062-3","article-title":"Transfer learning for clinical time series analysis using deep neural networks","volume":"4","author":"Gupta","year":"2020","journal-title":"J Healthc Inform Res"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"102036","DOI":"10.1016\/j.artmed.2021.102036","article-title":"DeepAISE\u2014an interpretable and recurrent neural survival model for early prediction of sepsis","volume":"113","author":"Shashikumar","year":"2021","journal-title":"Artif Intell Med"},{"key":"ref95","first-page":"20220212","article-title":"A multiorder feature tracking and explanation strategy for explainable deep learning","volume":"32","author":"Zheng","year":"2023","journal-title":"J Intell Syst"},{"key":"ref96","doi-asserted-by":"crossref","first-page":"e1914645","DOI":"10.1001\/jamanetworkopen.2019.14645","article-title":"Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides","volume":"2","author":"Tomita","year":"2019","journal-title":"JAMA Netw Open"},{"key":"ref97","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"ref98","doi-asserted-by":"crossref","first-page":"14297","DOI":"10.1038\/s41598-022-18293-7","article-title":"A lightweight hybrid deep learning system for cardiac valvular disease classification","volume":"12","author":"Al-Issa","year":"2022","journal-title":"Sci Rep"},{"key":"ref99","author":"Gulli","year":"2017","journal-title":"Deep learning with Keras"},{"key":"ref100","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"npj Digit Med"},{"key":"ref101","series-title":"2022 Opportunity Research Scholars Symposium (ORSS)","first-page":"23","article-title":"A comparison of CNNs and LSTMs for EEG signal classification","author":"Ting","year":"2022 Apr 27\u201327"},{"key":"ref102","unstructured":"Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv:1711.05225. 2017."},{"key":"ref103","series-title":"33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI, 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019","first-page":"590","article-title":"CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison","author":"Irvin","year":"2019"},{"key":"ref104","first-page":"5999","author":"Vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref105","author":"Lundberg","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref106","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"npj Digit Med"},{"key":"ref107","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"2999","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017 Oct 22\u201329"},{"key":"ref108","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat Med"},{"key":"ref109","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref110","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MSP.2017.2765695","article-title":"Model compression and acceleration for deep neural networks: the principles, progress, and challenges","volume":"35","author":"Cheng","year":"2018","journal-title":"IEEE Signal Process Mag"},{"key":"ref111","series-title":"5th International Conference on Learning Representations, ICLR 2017-Conference Track Proceedings","first-page":"1","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"Molchanov","year":"2016"},{"key":"ref112","first-page":"106123","article-title":"From scratch or pretrained? An in-depth analysis of deep learning approaches with limited data","volume":"150","author":"Sabha","year":"2024","journal-title":"Int J Syst Assur Eng Manag"},{"key":"ref113","series-title":"3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"ref114","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.1038\/s41598-021-84374-8","article-title":"Transfer learning for ECG classification","volume":"11","author":"Weimann","year":"2021","journal-title":"Sci Rep"},{"key":"ref115","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"4","author":"Yosinski","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref116","doi-asserted-by":"crossref","first-page":"3015","DOI":"10.1109\/TNSRE.2023.3295453","article-title":"Transfer learning on electromyography (EMG) tasks: approaches and beyond","volume":"31","author":"Wu","year":"2023","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"ref117","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/978-3-030-00919-9_29","author":"Tang","year":"2018","journal-title":"Machine learning in medical imaging"},{"key":"ref118","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2497","article-title":"Learning to read chest X-rays: recurrent neural cascade model for automated image annotation","author":"Shin","year":"2016 Jun 27\u201330"},{"key":"ref119","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/978-3-031-15937-4_65","volume":"13532","author":"Li","year":"2022 Jul","journal-title":"Artificial neural networks and machine learning\u2013ICANN 2022"},{"key":"ref120","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/978-3-031-26284-5_5","volume":"13842","author":"Yan","year":"2023","journal-title":"Computer vision\u2013ACCV 2022"},{"key":"ref121","doi-asserted-by":"crossref","first-page":"10692","DOI":"10.1007\/s10489-021-03010-0","article-title":"HT-Net: hierarchical context-attention transformer network for medical ct image segmentation","volume":"52","author":"Ma","year":"2022","journal-title":"Appl Intell"},{"key":"ref122","doi-asserted-by":"crossref","first-page":"822810","DOI":"10.3389\/fmolb.2022.822810","article-title":"Transformer-based high-frequency oscillation signal detection on magnetoencephalography from epileptic patients","volume":"9","author":"Guo","year":"2022","journal-title":"Front Mol Biosci"},{"key":"ref123","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s12911-021-01546-2","article-title":"Constrained transformer network for ECG signal processing and arrhythmia classification","volume":"21","author":"Che","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref124","doi-asserted-by":"crossref","first-page":"143149","DOI":"10.1109\/ACCESS.2024.3467181","article-title":"TransMixer-AF: advanced real-time detection of atrial fibrillation utilizing single-lead electrocardiogram signals","volume":"12","author":"Mahim","year":"2024","journal-title":"IEEE Access"},{"key":"ref125","doi-asserted-by":"crossref","first-page":"14605","DOI":"10.1038\/s41598-023-41314-y","article-title":"Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson\u2019s disease","volume":"13","author":"Lilhore","year":"2023","journal-title":"Sci Rep"},{"key":"ref126","doi-asserted-by":"crossref","first-page":"665","DOI":"10.3390\/electronics13030665","article-title":"Res-BiANet: a hybrid deep learning model for arrhythmia detection based on PPG signal","volume":"13","author":"Wu","year":"2024","journal-title":"Electronics"},{"key":"ref127","doi-asserted-by":"crossref","first-page":"102231","DOI":"10.1016\/j.bspc.2020.102231","volume":"63","author":"Kilicarslan","year":"2021","journal-title":"Biomed Signal Process Control"},{"key":"ref128","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1186\/s12859-023-05557-w","article-title":"Hybrid deep learning approach to improve classification of low-volume high-dimensional data","volume":"24","author":"Mavaie","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"ref129","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","author":"Woo","year":"2018","journal-title":"Computer vision\u2013ECCV 2018"},{"key":"ref130","first-page":"613","author":"Park","year":"2022","journal-title":"Medical image computing and computer assisted intervention-MICCAI 2022"},{"key":"ref131","doi-asserted-by":"crossref","first-page":"909023","DOI":"10.3389\/fbioe.2022.909023","article-title":"Improved multi-stream convolutional block attention module for sEMG-based gesture recognition","volume":"10","author":"Wang","year":"2022","journal-title":"Front Bioeng Biotechnol"},{"key":"ref132","series-title":"2019 Chinese Automation Congress (CAC)","first-page":"4712","article-title":"Hierarchical attention networks for image classification of remote sensing images based on visual Q&A methods","author":"Li","year":"2019 Nov 22\u201324"},{"key":"ref133","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","first-page":"1277","article-title":"Biomedical event extraction with hierarchical knowledge graphs","author":"Huang","year":"2020"},{"key":"ref134","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1186\/s12859-020-03673-5","article-title":"Biomedical document triage using a hierarchical attention-based capsule network","volume":"21","author":"Wang","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"ref135","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int J Comput Vis"},{"key":"ref136","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1007\/s00034-022-02265-3","article-title":"Deep convolutional neural network-based framework in the automatic diagnosis of migraine","volume":"42","author":"Aslan","year":"2023","journal-title":"Circuits Syst Signal Process"},{"key":"ref137","doi-asserted-by":"crossref","first-page":"38923","DOI":"10.1109\/ACCESS.2022.3165966","article-title":"Application of CNN for detection and localization of STEMI using 12-lead ECG images","volume":"10","author":"Kavak","year":"2022","journal-title":"IEEE Access"},{"key":"ref138","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1186\/s13059-020-02055-7","article-title":"Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study","volume":"21","author":"Jha","year":"2020","journal-title":"Genome Biol"},{"key":"ref139","doi-asserted-by":"crossref","first-page":"849","DOI":"10.3390\/brainsci12070849","article-title":"Compensated integrated gradients for reliable explanation of electroencephalogram signal classification","volume":"12","author":"Kawai","year":"2022","journal-title":"Brain Sci"},{"key":"ref140","series-title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","first-page":"5572","article-title":"The shapley value in machine learning","author":"Rozemberczki","year":"2022 Jul 23\u201329"},{"key":"ref141","doi-asserted-by":"crossref","first-page":"e0277975","DOI":"10.1371\/journal.pone.0277975","article-title":"When less is more powerful: shapley value attributed ablation with augmented learning for practical time series sensor data classification","volume":"17","author":"Ukil","year":"2022","journal-title":"PLoS One"},{"key":"ref142","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1186\/s12911-021-01569-9","volume":"21","author":"Roder","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref143","doi-asserted-by":"crossref","first-page":"102373","DOI":"10.1016\/j.isci.2021.102373","article-title":"Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram","volume":"24","author":"Zhang","year":"2021","journal-title":"iScience"},{"key":"ref144","doi-asserted-by":"crossref","DOI":"10.1002\/9781119785750","author":"Tyagi","year":"2021","journal-title":"Computational analysis and deep learning for medical care: principles, methods, and applications"},{"key":"ref145","doi-asserted-by":"crossref","first-page":"104629","DOI":"10.1016\/j.jbi.2024.104629","article-title":"Computational frameworks integrating deep learning and statistical models in mining multimodal omics data","volume":"152","author":"Lac","year":"2024","journal-title":"J Biomed Inform"},{"key":"ref146","doi-asserted-by":"crossref","first-page":"907150","DOI":"10.3389\/fmolb.2022.907150","article-title":"A comprehensive survey on computational learning methods for analysis of gene expression data","volume":"9","author":"Bhandari","year":"2022","journal-title":"Front Mol Biosci"},{"key":"ref147","doi-asserted-by":"crossref","first-page":"366","DOI":"10.55730\/1300-0152.2671","article-title":"Deep learning in bioinformatics","volume":"47","author":"Yousef","year":"2023","journal-title":"Turk J Biol"},{"key":"ref148","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref149","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"key":"ref150","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/10.867928","article-title":"Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG","volume":"47","author":"Kemp","year":"2000","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref151","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TNSRE.2003.814453","article-title":"A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces","volume":"11","author":"Sajda","year":"2003","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"ref152","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1109\/TBME.2004.826692","article-title":"The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials","volume":"51","author":"Blankertz","year":"2004","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref153","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/TNSRE.2006.875642","article-title":"The BCI competition III: validating alternative approaches to actual BCI problems","volume":"14","author":"Blankertz","year":"2006","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"ref154","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci Data"},{"key":"ref155","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.yebeh.2004.05.005","article-title":"Patient-specific seizure onset detection","volume":"5","author":"Shoeb","year":"2004","journal-title":"Epilepsy Behav"},{"key":"ref156","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","article-title":"IEMOCAP: interactive emotional dyadic motion capture database","volume":"42","author":"Busso","year":"2008","journal-title":"Lang Resour Eval"},{"key":"ref157","doi-asserted-by":"crossref","first-page":"49265","DOI":"10.1109\/ACCESS.2022.3172954","article-title":"Robust speech emotion recognition using CNN+LSTM based on stochastic fractal search optimization algorithm","volume":"10","author":"Abdelhamid","year":"2022","journal-title":"IEEE Access"},{"key":"ref158","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"14752","article-title":"MultiEMO: an attention-based correlation-aware multimodal fusion framework for emotion recognition in conversations","author":"Shi","year":"2023"},{"key":"ref159","doi-asserted-by":"crossref","first-page":"121692","DOI":"10.1016\/j.eswa.2023.121692","article-title":"Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: a systematic review of recent advancements and future prospects","volume":"237","author":"Zhang","year":"2024","journal-title":"Expert Syst Appl"},{"key":"ref160","doi-asserted-by":"crossref","first-page":"13405","DOI":"10.1007\/s00500-022-07499-6","article-title":"Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds","volume":"26","author":"Alqudah","year":"2022","journal-title":"Soft Comput"},{"key":"ref161","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3389\/fnins.2016.00196","article-title":"The temple university hospital EEG data corpus","volume":"10","author":"Obeid","year":"2016","journal-title":"Front Neurosci"},{"key":"ref162","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/TBME.2013.2246160","article-title":"Multiparameter respiratory rate estimation from the photoplethysmogram","volume":"60","author":"Karlen","year":"2013","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref163","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1093\/jamia\/ocy064","article-title":"The national sleep research resource: towards a sleep data commons","volume":"25","author":"Zhang","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"ref164","doi-asserted-by":"crossref","first-page":"192","DOI":"10.21437\/Interspeech.2018-1185","author":"Chanchaochai","year":"2018","journal-title":"Interspeech 2018"},{"key":"ref165","unstructured":"Yang J, Soh M, Lieu V, Weber DJ, Erickson Z. EMGBench: benchmarking out-of-distribution generalization and adaptation for electromyography. arXiv:2410.23625. 2024."},{"key":"ref166","author":"Charlton","year":"2024","journal-title":"MESA polysomnography dataset"},{"key":"ref167","first-page":"105004","article-title":"PPG-DaLiA: a dataset for activity monitoring using photoplethysmography","volume":"39","author":"Charlton","year":"2018","journal-title":"Physiol Meas"},{"key":"ref168","first-page":"115007","article-title":"PPG Diary: a dataset for continuous PPG monitoring","volume":"40","author":"Hahn","year":"2019","journal-title":"Physiol Meas"},{"key":"ref169","doi-asserted-by":"crossref","first-page":"H1062","DOI":"10.1152\/ajpheart.00218.2019","article-title":"Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes","volume":"317","author":"Charlton","year":"2019 Nov","journal-title":"Am J Physiol Heart Circ Physiol"},{"key":"ref170","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1088\/0967-3334\/37\/4\/610","article-title":"An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram","volume":"37","author":"Charlton","year":"2016 Apr","journal-title":"Physiol Meas"},{"key":"ref171","series-title":"Proceedings of the 20th ACM International Conference on Multimodal Interaction","first-page":"400","article-title":"Introducing WESAD, a multimodal dataset for wearable stress and affect detection","author":"Schmidt","year":"2018"},{"key":"ref172","series-title":"Computing in Cardiology Conference (CinC)","doi-asserted-by":"crossref","first-page":"1","DOI":"10.22489\/CinC.2017.065-469","article-title":"AF classification from a short single lead ECG recording: the physionet computing in cardiology challenge 2017","author":"Clifford","year":"2017"},{"key":"ref173","author":"Subasi","year":"2019","journal-title":"Practical guide for biomedical signals analysis using machine learning techniques: a MATLAB based approach.Cambridge"},{"key":"ref174","doi-asserted-by":"crossref","first-page":"102194","DOI":"10.1016\/j.bspc.2020.102194","article-title":"Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets","volume":"63","author":"Petmezas","year":"2021","journal-title":"Biomed Signal Process Control"},{"key":"ref175","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.comnet.2016.12.019","article-title":"ViSiBiD: a learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data","volume":"113","author":"Forkan","year":"2017","journal-title":"Comput Netw"},{"key":"ref176","doi-asserted-by":"crossref","first-page":"144180","DOI":"10.1109\/ACCESS.2023.3344531","article-title":"Applications of self-supervised learning to biomedical signals: a survey","volume":"11","author":"Del Pup","year":"2023","journal-title":"IEEE Access"},{"key":"ref177","article-title":"Transformer models for processing biological signal","author":"Kuzmanov","year":"2023 Jun","journal-title":"The 20th International Conference on Informatics and Information Technologies\u2019CIIT 2023"},{"key":"ref178","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc IEEE"},{"key":"ref179","series-title":"34th International Conference on Machine Learning, ICML 2017","first-page":"4573","article-title":"Asymmetric tri-training for unsupervised domain adaptation","author":"Saito","year":"2017"},{"key":"ref180","first-page":"1","volume":"1875","author":"Dietterich","year":"2000","journal-title":"Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref181","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"L\u00e4ngkvist","year":"2014","journal-title":"Pattern Recognit Lett"},{"key":"ref182","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Sch\u00f6lkopf","year":"2001","journal-title":"Neural Comput"},{"key":"ref183","series-title":"2008 Eighth IEEE International Conference on Data Mining","first-page":"413","article-title":"Isolation forest","author":"Liu","year":"2008 Dec 15\u201319"},{"key":"ref184","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/JBHI.2022.3215147","article-title":"Multi-learner based deep meta-learning for few-shot medical image classification","volume":"27","author":"Jiang","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref185","doi-asserted-by":"crossref","first-page":"904","DOI":"10.3390\/s22030904","article-title":"Study of the few-shot learning for ECG classification based on the PTB-XL dataset","volume":"22","author":"Pa\u0142czy\u0144ski","year":"2022","journal-title":"Sensors"},{"key":"ref186","series-title":"2021 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Demystification of few-shot and one-shot learning","author":"Tyukin","year":"2021 Jul 18\u201322"},{"key":"ref187","series-title":"Artificial General Intelligence: 10th International Conference, AGI 2017","first-page":"143","article-title":"One-shot ontogenetic learning in biomedical datastreams","author":"Kalantari","year":"2017 Aug 15\u201318"},{"key":"ref188","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3389\/fninf.2017.00041","article-title":"Feature selection methods for zero-shot learning of neural activity","volume":"11","author":"Caceres","year":"2017","journal-title":"Front Neuroinform"},{"key":"ref189","series-title":"2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)","first-page":"3337","article-title":"Medical image classification using generalized zero shot learning","author":"Mahapatra","year":"2021 Oct 11\u201317"},{"key":"ref190","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1109\/TSP.2021.3070186","article-title":"SR2CNN: zero-shot learning for signal recognition","volume":"69","author":"Dong","year":"2021","journal-title":"IEEE Trans Signal Process"},{"key":"ref191","doi-asserted-by":"crossref","first-page":"116912","DOI":"10.1016\/j.eswa.2022.116912","article-title":"Big data for healthcare industry 4.0: applications, challenges and future perspectives","volume":"200","author":"Karatas","year":"2022","journal-title":"Expert Syst Appl"},{"key":"ref192","doi-asserted-by":"crossref","first-page":"104227","DOI":"10.1016\/j.jbi.2022.104227","article-title":"Deep learning for rare disease: a scoping review","volume":"135","author":"Lee","year":"2022","journal-title":"J Biomed Inform"},{"key":"ref193","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1055\/s-0038-1641216","article-title":"Between access and privacy: challenges in sharing health data","volume":"27","author":"Malin","year":"2018","journal-title":"Yearb Med Inform"},{"key":"ref194","first-page":"371","article-title":"A tutorial on conformal prediction","volume":"9","author":"Shafer","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref195","series-title":"NIPS\u201920: Proceedings of the 34th International Conference on Neural Information Processing Systems","first-page":"3581","article-title":"Classification with valid and adaptive coverage","author":"Romano","year":"2020"},{"key":"ref196","author":"Gammerman","year":"2005","journal-title":"Algorithmic learning in a random world"},{"key":"ref197","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1080\/01621459.2017.1307116","article-title":"Distribution-free predictive inference for regression","volume":"113","author":"Lei","year":"2018","journal-title":"J Am Stat Assoc"},{"key":"ref198","first-page":"345","author":"Papadopoulos","year":"2002","journal-title":"Machine learning: ECML 2002"},{"key":"ref199","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1038\/s41746-019-0148-3","article-title":"Artificial intelligence and machine learning in clinical development: a translational perspective","volume":"2","author":"Shah","year":"2019","journal-title":"npj Digit Med"},{"key":"ref200","first-page":"832","article-title":"Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring","volume":"19","author":"Orphanidou","year":"2015","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref201","doi-asserted-by":"crossref","first-page":"103584","DOI":"10.1016\/j.bspc.2022.103584","article-title":"Explainable AI decision model for ECG data of cardiac disorders","volume":"75","author":"Anand","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"ref202","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","volume":"22","author":"Shickel","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref203","doi-asserted-by":"crossref","first-page":"102444","DOI":"10.1016\/j.media.2022.102444","article-title":"Recent advances and clinical applications of deep learning in medical image analysis","volume":"79","author":"Chen","year":"2022","journal-title":"Med Image Anal"},{"key":"ref204","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref205","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s12553-017-0179-1","article-title":"Google DeepMind and healthcare in an age of algorithms","volume":"7","author":"Powles","year":"2017","journal-title":"Health Technol"},{"key":"ref206","doi-asserted-by":"crossref","first-page":"1206139","DOI":"10.3389\/fdata.2023.1206139","article-title":"Artificial intelligence research strategy of the United States: critical assessment and policy recommendations","volume":"6","author":"Gursoy","year":"2023","journal-title":"Front Big Data"},{"key":"ref207","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1080\/13510347.2023.2196068","article-title":"Evaluating Europe\u2019s push to enact AI regulations: how will this influence global norms?","volume":"31","author":"Feldstein","year":"2024","journal-title":"Democratization"},{"key":"ref208","doi-asserted-by":"crossref","first-page":"104587","DOI":"10.1016\/j.bspc.2023.104587","article-title":"ECG signal generation based on conditional generative models","volume":"82","author":"Xia","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"ref209","doi-asserted-by":"crossref","first-page":"112225","DOI":"10.1016\/j.asoc.2024.112225","article-title":"Explainable AI-driven machine learning for heart disease detection using ECG signal","volume":"167","author":"Majhi","year":"2024","journal-title":"Appl Soft Comput"},{"key":"ref210","doi-asserted-by":"crossref","first-page":"110424","DOI":"10.1016\/j.patcog.2024.110424","article-title":"Federated learning for medical image analysis: a survey","volume":"151","author":"Guan","year":"2024","journal-title":"Pattern Recognit"},{"key":"ref211","unstructured":"McMahan HB, Moore E, Ramage D, Hampson S, Arcas BAY. Communication-efficient learning of deep networks from decentralized data. arXiv:1602.05629. 2016."},{"key":"ref212","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s44176-022-00006-z","article-title":"A brief review on algorithmic fairness","volume":"1","author":"Wang","year":"2022","journal-title":"Manag Syst Eng"},{"key":"ref213","unstructured":"Chen R, Yang J, Xiong H, Bai J, Hu T, Hao J, et al. Fast model debias with machine unlearning. arXiv:2310.12560. 2023."},{"key":"ref214","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1609\/icwsm.v15i1.18111","article-title":"Fair representation learning for heterogeneous information networks","volume":"15","author":"Zeng","year":"2021","journal-title":"Proc Int AAAI Conf Web Soc Medium"},{"key":"ref215","unstructured":"Jovanovi\u0107 N, Balunovi\u0107 M, Dimitrov DI, Vechev M. FARE: provably fair representation learning with practical certificates. arXiv:2210.07213. 2022."},{"key":"ref216","unstructured":"Begley T, Schwedes T, Frye C, Feige I. Explainability for fair machine learning. arXiv:2010.07389. 2020."},{"key":"ref217","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"3150","article-title":"Delayed impact of fair machine learning","author":"Liu","year":"2018 Jul 10\u201315"},{"key":"ref218","unstructured":"Corbett-Davies S, Gaebler JD, Nilforoshan H, Shroff R, Goel S, Nilforoshan H, et al. The measure and mismeasure of fairness. arXiv:1808.00023. 2018."},{"key":"ref219","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"1088","article-title":"Fair representation learning: an alternative to mutual information","author":"Liu","year":"2022"},{"key":"ref220","series-title":"Proceedings of the 36th International Conference on Machine Learning","first-page":"1436","article-title":"Flexibly fair representation learning by disentanglement","author":"Creager","year":"2019"},{"key":"ref221","series-title":"Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society","first-page":"263","article-title":"Costs and benefits of fair representation learning","author":"McNamara","year":"2019"},{"key":"ref222","doi-asserted-by":"crossref","first-page":"117689","DOI":"10.1016\/j.neuroimage.2020.117689","article-title":"Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal","volume":"228","author":"Dinsdale","year":"2021","journal-title":"Neuroimage"},{"key":"ref223","series-title":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","first-page":"721","article-title":"Federated learning aggregation: new robust algorithms with guarantees","author":"Ben Mansour","year":"2022 Dec 12\u201314"},{"key":"ref224","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467. 2016."},{"key":"ref225","series-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","first-page":"8026","article-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"ref226","series-title":"MM 2014\u2014Proceedings of the 2014 ACM Conference on Multimedia","first-page":"675","article-title":"Caffe: convolutional architecture for fast feature embedding","author":"Jia","year":"2014 Nov 3\u20137"},{"key":"ref227","unstructured":"Chen T, Li M, Li Y, Lin M, Wang N, Wang M, et al. MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv:1512.01274. 2015."},{"key":"ref228","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","article-title":"Real-time patient-specific ECG classification by 1-D convolutional neural networks","volume":"63","author":"Kiranyaz","year":"2016","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref229","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.artmed.2018.04.008","article-title":"Lung sounds classification using convolutional neural networks","volume":"88","author":"Bardou","year":"2018","journal-title":"Artif Intell Med"},{"key":"ref230","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/JBHI.2021.3085526","article-title":"Personalized blood pressure estimation using photoplethysmography: a transfer learning approach","volume":"26","author":"Leitner","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref231","doi-asserted-by":"crossref","first-page":"782367","DOI":"10.3389\/fnins.2022.782367","article-title":"Implementation of a convolutional neural network for eye blink artifacts removal from the electroencephalography signal","volume":"16","author":"Jurczak","year":"2022","journal-title":"Front Neurosci"},{"key":"ref232","doi-asserted-by":"crossref","first-page":"6939","DOI":"10.3390\/s24216939","article-title":"Coronary artery disease detection based on a novel multi-modal deep-coding method using ECG and PCG signals","volume":"24","author":"Sun","year":"2024","journal-title":"Sensors"},{"key":"ref233","doi-asserted-by":"crossref","first-page":"22696","DOI":"10.1038\/s41598-024-67729-9","article-title":"Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals","volume":"14","author":"Mortazavi","year":"2024","journal-title":"Sci Rep"},{"key":"ref234","doi-asserted-by":"crossref","first-page":"6369","DOI":"10.3390\/s21196369","article-title":"A muscle fatigue classification model based on LSTM and improved wavelet packet threshold","volume":"21","author":"Wang","year":"2021","journal-title":"Sensors"},{"key":"ref235","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.3390\/electronics11223749","article-title":"LSTM multi-stage transfer learning for blood pressure estimation using photoplethysmography","volume":"11","author":"Ali","year":"2022","journal-title":"Electronics"},{"key":"ref236","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.asoc.2024.111599","article-title":"An ensemble deep learning model for human activity analysis using wearable sensory data","volume":"159","author":"Batool","year":"2024","journal-title":"Appl Soft Comput"},{"key":"ref237","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.3390\/s22031232","article-title":"Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function","volume":"22","author":"Petmezas","year":"2022","journal-title":"Sensors"},{"key":"ref238","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.3390\/s16010115","article-title":"Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition","volume":"16","author":"Ord\u00f3\u00f1ez","year":"2016","journal-title":"Sensors"},{"key":"ref239","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3390\/bioengineering11010055","article-title":"An AI-enabled bias-free respiratory disease diagnosis model using cough audio","volume":"11","author":"Saeed","year":"2024","journal-title":"Bioengineering"},{"key":"ref240","doi-asserted-by":"crossref","first-page":"24441","DOI":"10.1038\/s41598-024-75531-w","article-title":"A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead ECG","volume":"14","author":"Bai","year":"2024","journal-title":"Sci Rep"},{"key":"ref241","doi-asserted-by":"crossref","first-page":"207914","DOI":"10.1109\/ACCESS.2020.3038422","article-title":"Recognition of muscle fatigue status based on improved wavelet threshold and CNN-SVM","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref242","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.3390\/sym16111413","article-title":"A framework for detecting pulmonary diseases from lung sound signals using a hybrid multi-task autoencoder-SVM model","volume":"16","author":"Orkweha","year":"2024","journal-title":"Symmetry"},{"key":"ref243","doi-asserted-by":"crossref","first-page":"1246746","DOI":"10.3389\/fphys.2023.1246746","article-title":"Deep learning for ECG arrhythmia detection and classification: an overview of progress for period 2017\u20132023","volume":"14","author":"Ansari","year":"2023","journal-title":"Front Physiol"},{"key":"ref244","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555. 2014."},{"key":"ref245","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/JBHI.2023.3248265","article-title":"Automatic wheeze segmentation using harmonic-percussive source separation and empirical mode decomposition","volume":"27","author":"Rocha","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref246","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1093\/jlb\/lsz013","article-title":"Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers?","volume":"6","author":"Forcier","year":"2019","journal-title":"J Law Biosci"},{"key":"ref247","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ahj.2018.09.002","article-title":"Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the Apple Heart Study","volume":"207","author":"Turakhia","year":"2019","journal-title":"Am Heart J"},{"key":"ref248","doi-asserted-by":"crossref","first-page":"e2225","DOI":"10.1002\/nop2.2225","article-title":"Evaluating the use of the mobile electrocardiogram technology KardiaMobileTM in community settings: an online survey","volume":"11","author":"Emmett","year":"2024","journal-title":"Nurs Open"},{"key":"ref249","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.18632\/oncotarget.26797","article-title":"Clinical validation of the tempus xT next-generation targeted oncology sequencing assay","volume":"10","author":"Beaubier","year":"2019","journal-title":"Oncotarget"},{"key":"ref250","doi-asserted-by":"crossref","first-page":"e50983","DOI":"10.2196\/50983","article-title":"Accuracy of 11 wearable, nearable, and airable consumer sleep trackers: prospective multicenter validation study","volume":"11","author":"Lee","year":"2023","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref251","doi-asserted-by":"crossref","first-page":"95.e11","DOI":"10.1016\/j.amjmed.2013.10.003","article-title":"Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring","volume":"127","author":"Barrett","year":"2014","journal-title":"Am J Med"},{"key":"ref252","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"key":"ref253","doi-asserted-by":"crossref","first-page":"10949","DOI":"10.1038\/s41598-021-90285-5","article-title":"Explaining deep neural networks for knowledge discovery in electrocardiogram analysis","volume":"11","author":"Hicks","year":"2021 May","journal-title":"Sci Rep"},{"key":"ref254","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1135","article-title":"Why should I trust you?: explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"ref255","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.csbj.2018.01.001","article-title":"Machine learning methods for histopathological image analysis","volume":"16","author":"Komura","year":"2018","journal-title":"Comput Struct Biotechnol J"},{"key":"ref256","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s11633-022-1398-0","article-title":"Federated learning on multimodal data: a comprehensive survey","volume":"20","author":"Lin","year":"2023","journal-title":"Mach Intell Res"},{"key":"ref257","doi-asserted-by":"crossref","first-page":"107051","DOI":"10.1016\/j.compbiomed.2023.107051","article-title":"Perspective of artificial intelligence in healthcare data management: a journey towards precision medicine","volume":"162","author":"Gupta","year":"2023","journal-title":"Comput Biol Med"},{"key":"ref258","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.tcm.2022.01.010","article-title":"Successfully implemented artificial intelligence and machine learning applications in cardiology: state-of-the-art review","volume":"33","author":"Van den Eynde","year":"2023","journal-title":"Trends Cardiovasc Med"},{"key":"ref259","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1002\/cpt.3198","article-title":"Artificial intelligence\/machine learning: the new frontier of clinical pharmacology and precision medicine","volume":"115","author":"Liu","year":"2024","journal-title":"Clin Pharmacol Ther"},{"key":"ref260","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1007\/s00247-021-05114-8","article-title":"How does artificial intelligence in radiology improve efficiency and health outcomes?","volume":"52","author":"van Leeuwen","year":"2022","journal-title":"Pediatr Radiol"},{"key":"ref261","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1186\/s12909-023-04698-z","article-title":"Revolutionizing healthcare: the role of artificial intelligence in clinical practice","volume":"23","author":"Alowais","year":"2023","journal-title":"BMC Med Educ"},{"key":"ref262","doi-asserted-by":"crossref","first-page":"e25759","DOI":"10.2196\/25759","article-title":"Role of artificial intelligence applications in real-life clinical practice: systematic review","volume":"23","author":"Yin","year":"2021","journal-title":"J Med Internet Res"},{"key":"ref263","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.semcancer.2023.09.005","article-title":"Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology","volume":"96","author":"Jiang","year":"2023","journal-title":"Semin Cancer Biol"},{"key":"ref264","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s11596-021-2485-0","article-title":"Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare","volume":"41","author":"Xie","year":"2021","journal-title":"Curr Med Sci"},{"key":"ref265","doi-asserted-by":"crossref","first-page":"1169595","DOI":"10.3389\/frai.2023.1169595","article-title":"ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations","volume":"6","author":"Dave","year":"2023","journal-title":"Front Artif Intell"}],"container-title":["Computers, Materials & 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