{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T01:02:33Z","timestamp":1792458153301,"version":"4.5.0"},"reference-count":83,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2024,5,10]]},"abstract":"<jats:p>BACKGROUND: Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds. OBJECTIVE: The study intends to develop a novel blended ensemble model using hybrid deep learning models and softmax regression to classify adult, and paediatric heart sounds into five distinct classes, distinguishing itself as a groundbreaking work in this domain. Furthermore, the research aims to create a comprehensive 5-class paediatric phonocardiogram (PCG) dataset. The dataset includes two critical pathological classes, namely atrial septal defects and ventricular septal defects, along with functional murmurs, pathological and normal heart sounds. METHODS: The work proposes a blended ensemble model (HbNet-Heartbeat Network) comprising two hybrid models, CNN-BiLSTM and CNN-LSTM, as base models and Softmax regression as meta-learner. HbNet leverages the strengths of base models and improves the overall PCG classification accuracy. Mel Frequency Cepstral Coefficients (MFCC) capture the crucial audio signal characteristics relevant to the classification. The amalgamation of these two deep learning structures enhances the precision and reliability of PCG classification, leading to improved diagnostic results. RESULTS: The HbNet model exhibited excellent results with an average accuracy of 99.72% and sensitivity of 99.3% on an adult dataset, surpassing all the existing state-of-the-art works. The researchers have validated the reliability of the HbNet model by testing it on a real-time paediatric dataset. The paediatric model\u2019s accuracy is 86.5%. HbNet detected functional murmur with 100% precision. CONCLUSION: The results indicate that the HbNet model exhibits a high level of efficacy in the early detection of cardiac disorders. Results also imply that HbNet has the potential to serve as a valuable tool for the development of decision-support systems that aid medical practitioners in confirming their diagnoses. This method makes it easier for medical professionals to diagnose and initiate prompt treatment while performing preliminary auscultation and reduces unnecessary echocardiograms.<\/jats:p>","DOI":"10.3233\/thc-231290","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T11:36:47Z","timestamp":1708688207000},"page":"1925-1945","source":"Crossref","is-referenced-by-count":9,"title":["HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram"],"prefix":"10.1177","volume":"32","author":[{"given":"Ann Nita","family":"Netto","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, LBS Institute of Technology for Women, APJ Abdul Kalam Technological University, Trivandrum, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizy","family":"Abraham","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, LBS Institute of Technology for Women, APJ Abdul Kalam Technological University, Trivandrum, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saji","family":"Philip","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Thiruvalla Medical Mission Hospital, Thiruvalla, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"12","key":"10.3233\/THC-231290_ref2","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1038\/S41569-021-00570-Z","article-title":"Global epidemiology of valvular heart disease","volume":"18","author":"Coffey","year":"2021","journal-title":"Nature Reviews Cardiology."},{"key":"10.3233\/THC-231290_ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10346-019-01286-5"},{"key":"10.3233\/THC-231290_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-019-8208-z"},{"key":"10.3233\/THC-231290_ref5","doi-asserted-by":"publisher","DOI":"10.3390\/app8122344"},{"key":"10.3233\/THC-231290_ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4972346"},{"key":"10.3233\/THC-231290_ref7","doi-asserted-by":"publisher","first-page":"227","DOI":"10.12915\/PE.2014.11.58","article-title":"Adaptive Neural Network Filter for denoising the phonocardiography signal","volume":"90","author":"Gradolewski","year":"2014","journal-title":"Przeglad Elektrotechniczny."},{"key":"10.3233\/THC-231290_ref9","doi-asserted-by":"publisher","DOI":"10.1109\/APCCAS.2010.5775018"},{"issue":"3","key":"10.3233\/THC-231290_ref10","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1109\/TBME.2008.2002141","article-title":"Sequential blind source extraction for quasi-periodic signals with time-varying period","volume":"56","author":"Tsalaile","year":"2009","journal-title":"IEEE Transactions on Biomedical Engineering."},{"issue":"3","key":"10.3233\/THC-231290_ref11","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/J.IRBM.2013.01.017","article-title":"Bayesian denoising framework of phonocardiogram based on a new dynamical model","volume":"34","author":"Almasi","year":"2013","journal-title":"IRBM."},{"key":"10.3233\/THC-231290_ref12","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1016\/J.PHPRO.2012.03.157","article-title":"Research and implementation of heart sound denoising","volume":"25","author":"Liu","year":"2012","journal-title":"Physics Procedia."},{"key":"10.3233\/THC-231290_ref13","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/505840"},{"key":"10.3233\/THC-231290_ref14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/J.BSPC.2017.08.027","article-title":"Adaptive overlapping-group sparse denoising for heart sound signals","volume":"40","author":"Deng","year":"2018","journal-title":"Biomedical Signal Processing and Control."},{"issue":"38","key":"10.3233\/THC-231290_ref15","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/J.BSPC.2017.07.002","article-title":"An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal","volume":"C","author":"Jain","year":"2017","journal-title":"Biomedical Signal Processing and Control."},{"key":"10.3233\/THC-231290_ref16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/J.ARTMED.2017.05.005","article-title":"Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods","volume":"78","author":"Eslamizadeh","year":"2017","journal-title":"Artificial Intelligence in Medicine."},{"key":"10.3233\/THC-231290_ref17","doi-asserted-by":"publisher","DOI":"10.3390\/S19040957"},{"key":"10.3233\/THC-231290_ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICEE52715.2021.9544365"},{"issue":"2","key":"10.3233\/THC-231290_ref19","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TBME.2016.2559800","article-title":"S1 and S2 heart sound recognition using deep neural networks","volume":"64","author":"Chen","year":"2017","journal-title":"IEEE Transactions on Biomedical Engineering."},{"key":"10.3233\/THC-231290_ref20","doi-asserted-by":"publisher","first-page":"100283","DOI":"10.1016\/J.SMHL.2022.100283","article-title":"A critical review of heart sound signal segmentation algorithms","volume":"24","author":"Milani","year":"2022","journal-title":"Smart Health."},{"issue":"3","key":"10.3233\/THC-231290_ref21","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/S40846-017-0320-7","article-title":"A robust algorithm for segmentation of phonocardiography signal using tunable quality wavelet transform","volume":"38","author":"Jain","year":"2017","journal-title":"Journal of Medical and Biological Engineering 2017 38: 3"},{"issue":"13","key":"10.3233\/THC-231290_ref22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20133790","article-title":"Phonocardiogram signal processing for automatic diagnosis of congenital heart disorders through fusion of temporal and cepstral features","volume":"20","author":"Aziz","year":"2020","journal-title":"Sensors (Switzerland)."},{"key":"10.3233\/THC-231290_ref23","doi-asserted-by":"publisher","first-page":"103445","DOI":"10.1016\/J.BSPC.2021.103445","article-title":"Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features","volume":"73","author":"Khan","year":"2022","journal-title":"Biomedical Signal Processing and Control."},{"issue":"12","key":"10.3233\/THC-231290_ref24","doi-asserted-by":"publisher","first-page":"3861","DOI":"10.1109\/JSEN.2017.2694970","article-title":"Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope","volume":"17","author":"Nivitha Varghees","year":"2017","journal-title":"IEEE Sensors Journal."},{"key":"10.3233\/THC-231290_ref25","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9640821"},{"key":"10.3233\/THC-231290_ref26","doi-asserted-by":"publisher","first-page":"102257","DOI":"10.1016\/J.ARTMED.2022.102257","article-title":"Automatic pediatric congenital heart disease classification based on heart sound signal","volume":"126","author":"Xu","year":"2022","journal-title":"Artificial Intelligence in Medicine."},{"key":"10.3233\/THC-231290_ref27","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512284"},{"issue":"4","key":"10.3233\/THC-231290_ref28","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1504\/IJBET.2016.076609","article-title":"Algorithm for the estimation of pulmonary hypertension by the heart sounds using a Hilbert transform","volume":"20","author":"Cherif","year":"2016","journal-title":"International Journal of Biomedical Engineering and Technology."},{"key":"10.3233\/THC-231290_ref29","doi-asserted-by":"publisher","DOI":"10.21817\/ijet\/2017\/v9i2\/170902219"},{"key":"10.3233\/THC-231290_ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3364908.3364924"},{"key":"10.3233\/THC-231290_ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00237"},{"key":"10.3233\/THC-231290_ref32","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/J.BSPC.2018.10.018","article-title":"Multi-centroid diastolic duration distribution based HSMM for heart sound segmentation","volume":"48","author":"Kamson","year":"2019","journal-title":"Biomedical Signal Processing and Control."},{"issue":"2","key":"10.3233\/THC-231290_ref33","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1109\/JBHI.2018.2841197","article-title":"Adaptive sojourn time HSMM for heart sound segmentation","volume":"23","author":"Oliveira","year":"2019","journal-title":"IEEE Journal of Biomedical and Health Informatics."},{"issue":"6","key":"10.3233\/THC-231290_ref34","doi-asserted-by":"publisher","first-page":"2435","DOI":"10.1109\/JBHI.2019.2894222","article-title":"Deep convolutional neural networks for heart sound segmentation","volume":"23","author":"Renna","year":"2019","journal-title":"IEEE Journal of Biomedical and Health Informatics."},{"key":"10.3233\/THC-231290_ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2843258"},{"issue":"6","key":"10.3233\/THC-231290_ref36","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1109\/JBHI.2019.2949516","article-title":"Heart sound segmentation using bidirectional LSTMs with attention","volume":"24","author":"Fernando","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics."},{"issue":"4","key":"10.3233\/THC-231290_ref37","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1007\/S40747-021-00325-W","article-title":"End-to-end heart sound segmentation using deep convolutional recurrent network","volume":"7","author":"Chen","year":"2021","journal-title":"Complex & Intelligent Systems 2021 7: 4"},{"key":"10.3233\/THC-231290_ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1286-5"},{"key":"10.3233\/THC-231290_ref39","doi-asserted-by":"publisher","DOI":"10.1142\/S0219519419500258"},{"issue":"8","key":"10.3233\/THC-231290_ref40","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1088\/1361-6579\/AA7623","article-title":"Combining sparse coding and time-domain features for heart sound classification","volume":"38","author":"Whitaker","year":"2017","journal-title":"Physiological Measurement."},{"key":"10.3233\/THC-231290_ref41","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/J.BSPC.2017.08.002","article-title":"Classification of heart sound signal using curve fitting and fractal dimension","volume":"39","author":"Hamidi","year":"2018","journal-title":"Biomedical Signal Processing and Control."},{"issue":"5","key":"10.3233\/THC-231290_ref42","doi-asserted-by":"publisher","first-page":"472","DOI":"10.3390\/E21050472","article-title":"Classification of heart sounds based on the wavelet fractal and twin support vector machine","volume":"21","author":"Li","year":"2019","journal-title":"Entropy 2019, Vol 21, Page 472"},{"issue":"2","key":"10.3233\/THC-231290_ref43","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10470-021-01867-2","article-title":"Classification of normal\/abnormal PCG recordings using a time-frequency approach","volume":"109","author":"Hazeri","year":"2021","journal-title":"Analog Integrated Circuits and Signal Processing."},{"key":"10.3233\/THC-231290_ref44","doi-asserted-by":"publisher","DOI":"10.22489\/CinC.2016.235-109"},{"issue":"1","key":"10.3233\/THC-231290_ref45","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/S13534-017-0046-Z","article-title":"Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks","volume":"8","author":"Beritelli","year":"2017","journal-title":"Biomedical Engineering Letters 2017 8: 1"},{"key":"10.3233\/THC-231290_ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09875-w"},{"key":"10.3233\/THC-231290_ref47","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3233\/SHTI200146","article-title":"Distinguishing septal heart defects from the valvular regurgitation using intelligent phonocardiography","volume":"270","author":"Gharehbaghi","year":"2020","journal-title":"Studies in Health Technology and Informatics."},{"key":"10.3233\/THC-231290_ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2021.12.019"},{"key":"10.3233\/THC-231290_ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105750"},{"key":"10.3233\/THC-231290_ref50","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-019-0651-3"},{"key":"10.3233\/THC-231290_ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-8055-5_47"},{"key":"10.3233\/THC-231290_ref52","doi-asserted-by":"publisher","first-page":"106727","DOI":"10.1016\/J.CMPB.2022.106727","article-title":"Cardi-Net: A deep neural network for classification of cardiac disease using phonocardiogram signal","volume":"219","author":"Khan","year":"2022","journal-title":"Computer Methods and Programs in Biomedicine."},{"key":"10.3233\/THC-231290_ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3274174"},{"key":"10.3233\/THC-231290_ref54","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2022.923956"},{"issue":"21","key":"10.3233\/THC-231290_ref55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s19214819","article-title":"Heartbeat sound signal classification using deep learning","volume":"19","author":"Raza","year":"2019","journal-title":"Sensors (Switzerland)."},{"key":"10.3233\/THC-231290_ref56","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab8770"},{"key":"10.3233\/THC-231290_ref57","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8856748"},{"issue":"8","key":"10.3233\/THC-231290_ref58","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.3390\/electronics11081246","article-title":"Automatic classification of normal-abnormal heart sounds using convolution neural network and long-short term memory","volume":"11","author":"Chen","year":"2022","journal-title":"Electronics."},{"key":"10.3233\/THC-231290_ref59","doi-asserted-by":"publisher","first-page":"36955","DOI":"10.1109\/ACCESS.2021.3063129","article-title":"CardioXNet: A novel lightweight deep learning framework for cardiovascular disease classification using heart sound recordings","volume":"9","author":"Shuvo","year":"2021","journal-title":"IEEE Access."},{"key":"10.3233\/THC-231290_ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICESC51422.2021.9532766"},{"key":"10.3233\/THC-231290_ref61","doi-asserted-by":"publisher","first-page":"105604","DOI":"10.1016\/j.cmpb.2020.105604","article-title":"Classification of heart sound signals using a novel deep WaveNet model","volume":"196","author":"Oh","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine."},{"key":"10.3233\/THC-231290_ref62","doi-asserted-by":"publisher","first-page":"105940","DOI":"10.1016\/J.CMPB.2021.105940","article-title":"Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings","volume":"200","author":"Alkhodari","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine."},{"issue":"1","key":"10.3233\/THC-231290_ref63","doi-asserted-by":"publisher","first-page":"1","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":"Scientific Reports 2022 12: 1"},{"key":"10.3233\/THC-231290_ref64","doi-asserted-by":"publisher","first-page":"104075","DOI":"10.1016\/J.BSPC.2022.104075","article-title":"PCG classification through spectrogram using transfer learning","volume":"79","author":"Ismail","year":"2023","journal-title":"Biomedical Signal Processing and Control."},{"key":"10.3233\/THC-231290_ref65","doi-asserted-by":"publisher","first-page":"109475","DOI":"10.1109\/ACCESS.2020.3002151","article-title":"Transfer learning benchmark for cardiovascular disease recognition","volume":"8","author":"Boulares","year":"2020","journal-title":"IEEE Access."},{"issue":"1","key":"10.3233\/THC-231290_ref66","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/J.BBE.2023.01.004","article-title":"PCG signal classification using a hybrid multi round transfer learning classifier","volume":"43","author":"Ismail","year":"2023","journal-title":"Biocybernetics and Biomedical Engineering."},{"key":"10.3233\/THC-231290_ref67","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12123109"},{"key":"10.3233\/THC-231290_ref68","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-819061-6.00006-9"},{"key":"10.3233\/THC-231290_ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-79766-9_7"},{"key":"10.3233\/THC-231290_ref70","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102019"},{"key":"10.3233\/THC-231290_ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682668"},{"issue":"2","key":"10.3233\/THC-231290_ref72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/healthcare9020169","article-title":"A framework for ai-assisted detection of patent ductus arteriosus from neonatal phonocardiogram","volume":"9","author":"G\u00f3mez-Quintana","year":"2021","journal-title":"Healthcare (Switzerland)."},{"key":"10.3233\/THC-231290_ref73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/OJIM.2023.3320765","article-title":"Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular","volume":"2","author":"Roy","year":"2023","journal-title":"IEEE Open Journal of Instrumentation and Measurement."},{"key":"10.3233\/THC-231290_ref74","doi-asserted-by":"publisher","DOI":"10.3389\/fmedt.2022.854382"},{"key":"10.3233\/THC-231290_ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICAI58407.2023.10136668"},{"key":"10.3233\/THC-231290_ref76","doi-asserted-by":"publisher","DOI":"10.1007\/S11045-023-00876-W"},{"issue":"2","key":"10.3233\/THC-231290_ref77","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TSM.2019.2904306","article-title":"A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing","volume":"32","author":"Saqlain","year":"2019","journal-title":"IEEE Transactions on Semiconductor Manufacturing."},{"issue":"15","key":"10.3233\/THC-231290_ref78","doi-asserted-by":"publisher","first-page":"7554","DOI":"10.3390\/APP12157554","article-title":"A soft-voting ensemble classifier for detecting patients affected by COVID-19","volume":"12","author":"Manconi","year":"2022","journal-title":"Applied Sciences 2022, Vol 12, Page 7554"},{"key":"10.3233\/THC-231290_ref79","doi-asserted-by":"publisher","first-page":"191100","DOI":"10.1109\/ACCESS.2020.3032226","article-title":"Environment sound classification based on visual multi-feature fusion and GRU-AWS","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/THC-231290_ref80","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-008-9611-z"},{"key":"10.3233\/THC-231290_ref81","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"10.3233\/THC-231290_ref82","doi-asserted-by":"publisher","first-page":"1041082","DOI":"10.3389\/FCVM.2022.1041082\/BIBTEX","article-title":"Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram","volume":"9","author":"Huang","year":"2022","journal-title":"Frontiers in Cardiovascular Medicine."},{"key":"10.3233\/THC-231290_ref83","doi-asserted-by":"publisher","DOI":"10.1109\/MECO49872.2020.9134333"},{"issue":"2","key":"10.3233\/THC-231290_ref84","first-page":"236","article-title":"From heart murmur to echocardiography congenital heart defects diagnostics using machine-learning algorithms","volume":"33","author":"Begic","year":"2021","journal-title":"Medicina Academica Mostariensia."},{"issue":"2","key":"10.3233\/THC-231290_ref85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/healthcare9020169","article-title":"A framework for ai-assisted detection of patent ductus arteriosus from neonatal phonocardiogram","volume":"9","author":"G\u00f3mez-Quintana","year":"2021","journal-title":"Healthcare (Switzerland)."}],"container-title":["Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/THC-231290","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:59:20Z","timestamp":1777615160000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/THC-231290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":83,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/thc-231290","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,10]]}}}