{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T19:53:37Z","timestamp":1784663617170,"version":"3.55.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-024-02822-7","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T11:46:19Z","timestamp":1735559179000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An improved electrocardiogram arrhythmia classification performance with feature optimization"],"prefix":"10.1186","volume":"24","author":[{"given":"Annisa","family":"Darmawahyuni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siti","family":"Nurmaini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bambang","family":"Tutuko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Naufal","family":"Rachmatullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firdaus","family":"Firdaus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anggun","family":"Islami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordan","family":"Marcelino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rendy","family":"Isdwanta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Ikhwan","family":"Perwira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"key":"2822_CR1","doi-asserted-by":"publisher","first-page":"1246746","DOI":"10.3389\/fphys.2023.1246746","volume":"14","author":"Y Ansari","year":"2023","unstructured":"Ansari Y, Mourad O, Qaraqe K, Serpedin E. Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017\u20132023. Front Physiol. 2023;14:1246746.","journal-title":"Front Physiol"},{"key":"2822_CR2","doi-asserted-by":"publisher","first-page":"104206","DOI":"10.1016\/j.bspc.2022.104206","volume":"79","author":"Y Wang","year":"2023","unstructured":"Wang Y, Yang G, Li S, Li Y, He L, Liu D. Arrhythmia classification algorithm based on multi-head self-attention mechanism. Biomed Signal Process Control. 2023;79:104206.","journal-title":"Biomed Signal Process Control"},{"key":"2822_CR3","doi-asserted-by":"publisher","first-page":"96","DOI":"10.37394\/23208.2021.18.11","volume":"18","author":"D Koppad","year":"2021","unstructured":"Koppad D. Arrhythmia classification using deep learning: a review. WSEAS Trans Biol Biomed. 2021;18:96\u2013105.","journal-title":"WSEAS Trans Biol Biomed"},{"key":"2822_CR4","doi-asserted-by":"crossref","unstructured":"Ebrahimi Z, Loni M, Daneshtalab M, Gharehbaghi A. A review on deep learning methods for ECG arrhythmia classification. Expert Systems with Applications: X.\u00a02020;7:100033.","DOI":"10.1016\/j.eswax.2020.100033"},{"key":"2822_CR5","doi-asserted-by":"publisher","unstructured":"B Remeseiro, V Bolon-Canedo. A review of feature selection methods in medical applications. Comput Biol Med. 2019;112. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103375.","DOI":"10.1016\/j.compbiomed.2019.103375"},{"key":"2822_CR6","doi-asserted-by":"publisher","unstructured":"M Alirezanejad, R Enayatifar, H Motameni, H Nematzadeh. Heuristic filter feature selection methods for medical datasets. Genomics. 2020;112(2). https:\/\/doi.org\/10.1016\/j.ygeno.2019.07.002.","DOI":"10.1016\/j.ygeno.2019.07.002"},{"key":"2822_CR7","doi-asserted-by":"crossref","unstructured":"Nargesian F, Samulowitz H, Khurana U, Khalil EB,\u00a0 Turaga DS. Learning Feature Engineering for Classification. In Ijcai. 2017;17:2529-35.","DOI":"10.24963\/ijcai.2017\/352"},{"issue":"6","key":"2822_CR8","first-page":"12","volume":"3","author":"NK Dewangan","year":"2015","unstructured":"Dewangan NK, Shukla SP. A survey on ECG signal feature extraction and analysis techniques. Int J Innov Res Electr Electron Instrum Control Eng. 2015;3(6):12\u20139.","journal-title":"Int J Innov Res Electr Electron Instrum Control Eng"},{"key":"2822_CR9","unstructured":"Karpagachelvi S, Arthanari M, Sivakumar M. ECG feature extraction techniques-a survey approach. Int J Comput Sci Inf Secur. 2010;8(1)."},{"key":"2822_CR10","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1007\/s40031-021-00606-5","volume":"102","author":"V Gupta","year":"2021","unstructured":"Gupta V, Mittal M, Mittal V, Saxena NK. A critical review of feature extraction techniques for ECG signal analysis. J Inst Eng Ser B. 2021;102:1049\u201360.","journal-title":"J Inst Eng Ser B"},{"key":"2822_CR11","first-page":"1","volume":"2021","author":"J Cai","year":"2021","unstructured":"Cai J, Zhou G, Dong M, Hu X, Liu G, Ni W. Real-time arrhythmia classification algorithm using time-domain ECG feature based on FFNN and CNN. Math Probl Eng. 2021;2021:1\u201317.","journal-title":"Math Probl Eng"},{"key":"2822_CR12","doi-asserted-by":"crossref","unstructured":"Xu Y, Zhang S, Cao Z, Chen Q, Xiao W. Extreme Learning Machine for Heartbeat Classification with Hybrid Time\u2010Domain and Wavelet Time\u2010Frequency Features. J Healthc Eng. 2021;(1):6674695.","DOI":"10.1155\/2021\/6674695"},{"issue":"01","key":"2822_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.32604\/jai.2021.014175","volume":"3","author":"T Vijayakumar","year":"2021","unstructured":"Vijayakumar T, Vinothkanna R, Duraipandian M. Fusion based feature extraction analysis of ECG signal interpretation\u2013a systematic approach. J Artif Intell. 2021;3(01):1\u201316.","journal-title":"J Artif Intell"},{"key":"2822_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-01337-1","volume":"20","author":"Y Hu","year":"2020","unstructured":"Hu Y, Zhao Y, Liu J, Pang J, Zhang C, Li P. An effective frequency-domain feature of atrial fibrillation based on time\u2013frequency analysis. BMC Med Inform Decis Mak. 2020;20:1\u201311.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"7","key":"2822_CR15","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1007\/s11760-020-01681-9","volume":"14","author":"R Lekhal","year":"2020","unstructured":"Lekhal R, Zidelmal Z, Ould-Abdesslam D. Optimized time\u2013frequency features and semi-supervised SVM to heartbeat classification. Signal, Image Video Process. 2020;14(7):1471\u20138.","journal-title":"Signal, Image Video Process"},{"issue":"6","key":"2822_CR16","first-page":"6598","volume":"10","author":"S Kuila","year":"2020","unstructured":"Kuila S, Dhanda N, Joardar S. Feature extraction of electrocardiogram signal using machine learning classification. Int J Electr Comput Eng. 2020;10(6):6598\u2013605.","journal-title":"Int J Electr Comput Eng"},{"key":"2822_CR17","doi-asserted-by":"publisher","first-page":"106621","DOI":"10.1016\/j.compeleceng.2020.106621","volume":"84","author":"I Kayikcioglu","year":"2020","unstructured":"Kayikcioglu I, Akdeniz F, K\u00f6se C, Kayikcioglu T. Time-frequency approach to ECG classification of myocardial infarction. Comput Electr Eng. 2020;84:106621.","journal-title":"Comput Electr Eng"},{"key":"2822_CR18","doi-asserted-by":"publisher","first-page":"103569","DOI":"10.1016\/j.bspc.2022.103569","volume":"75","author":"AJD Krupa","year":"2022","unstructured":"Krupa AJD, Dhanalakshmi S, Kumar R. Joint time-frequency analysis and non-linear estimation for fetal ECG extraction. Biomed Signal Process Control. 2022;75:103569.","journal-title":"Biomed Signal Process Control"},{"key":"2822_CR19","doi-asserted-by":"publisher","first-page":"105402","DOI":"10.1016\/j.knosys.2019.105402","volume":"190","author":"J Zhang","year":"2020","unstructured":"Zhang J, Tian J, Cao Y, Yang Y, Xu X. Deep time\u2013frequency representation and progressive decision fusion for ECG classification. Knowledge-based Syst. 2020;190:105402.","journal-title":"Knowledge-based Syst"},{"key":"2822_CR20","doi-asserted-by":"publisher","first-page":"107473","DOI":"10.1016\/j.knosys.2021.107473","volume":"232","author":"F Murat","year":"2021","unstructured":"Murat F, et al. Exploring deep features and ECG attributes to detect cardiac rhythm classes. Knowledge-Based Syst. 2021;232:107473.","journal-title":"Knowledge-Based Syst"},{"issue":"1","key":"2822_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s12938-023-01075-1","volume":"22","author":"AK Singh","year":"2023","unstructured":"Singh AK, Krishnan S. ECG signal feature extraction trends in methods and applications. Biomed Eng Online. 2023;22(1):22.","journal-title":"Biomed Eng Online"},{"key":"2822_CR22","doi-asserted-by":"crossref","unstructured":"Diker A, Engin AVCI. Feature extraction of ECG signal by using deep feature. In 2019 7th International Symposium on Digital Forensics and Security (ISDFS). Ieee; 2019.\u00a0pp. 1-6.","DOI":"10.1109\/ISDFS.2019.8757522"},{"key":"2822_CR23","doi-asserted-by":"publisher","first-page":"101896","DOI":"10.1016\/j.artmed.2020.101896","volume":"109","author":"X Wu","year":"2020","unstructured":"Wu X, Zheng Y, Chu CH, He Z. Extracting deep features from short ECG signals for early atrial fibrillation detection. Artif Intell Med. 2020;109:101896.","journal-title":"Artif Intell Med"},{"issue":"3","key":"2822_CR24","doi-asserted-by":"publisher","first-page":"2753","DOI":"10.3390\/ijerph20032753","volume":"20","author":"K Bannajak","year":"2023","unstructured":"Bannajak K, Theera-Umpon N, Auephanwiriyakul S. Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction. Int J Environ Res Public Health. 2023;20(3):2753.","journal-title":"Int J Environ Res Public Health"},{"key":"2822_CR25","doi-asserted-by":"publisher","first-page":"100330","DOI":"10.1016\/j.mlwa.2022.100330","volume":"9","author":"MM Hossain","year":"2022","unstructured":"Hossain MM, et al. Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease. Mach Learn with Appl. 2022;9:100330.","journal-title":"Mach Learn with Appl"},{"issue":"4","key":"2822_CR26","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.1080\/03772063.2020.1725663","volume":"68","author":"KK Patro","year":"2022","unstructured":"Patro KK, Jaya Prakash A, Jayamanmadha Rao M, Rajesh Kumar P. An efficient optimized feature selection with machine learning approach for ECG biometric recognition. IETE J Res. 2022;68(4):2743\u201354.","journal-title":"IETE J Res"},{"issue":"4","key":"2822_CR27","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3390\/bioengineering10040429","volume":"10","author":"M Hassaballah","year":"2023","unstructured":"Hassaballah M, Wazery YM, Ibrahim IE, Farag A. Ecg heartbeat classification using machine learning and metaheuristic optimization for smart healthcare systems. Bioengineering. 2023;10(4):429.","journal-title":"Bioengineering"},{"issue":"1","key":"2822_CR28","first-page":"26","volume":"35","author":"SM Qaisar","year":"2023","unstructured":"Qaisar SM, Khan SI, Srinivasan K, Krichen M. Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition. J King Saud Univ Inf Sci. 2023;35(1):26\u201337.","journal-title":"J King Saud Univ Inf Sci"},{"issue":"1","key":"2822_CR29","doi-asserted-by":"publisher","first-page":"147","DOI":"10.55525\/tjst.1324854","volume":"19","author":"M Tun\u00e7","year":"2024","unstructured":"Tun\u00e7 M, Cang\u00f6z GB. Classification of the cardiac arrhythmia using combined feature selection algorithms. Turkish J Sci Technol. 2024;19(1):147\u201359.","journal-title":"Turkish J Sci Technol"},{"key":"2822_CR30","doi-asserted-by":"publisher","first-page":"105565","DOI":"10.1016\/j.bspc.2023.105565","volume":"87","author":"WS Admass","year":"2024","unstructured":"Admass WS, Bogale GA. Arrhythmia classification using ECG signal: a meta-heuristic improvement of optimal weighted feature integration and attention-based hybrid deep learning model. Biomed Signal Process Control. 2024;87:105565.","journal-title":"Biomed Signal Process Control"},{"key":"2822_CR31","doi-asserted-by":"publisher","unstructured":"Darmawahyuni A et al. Health-related data analysis using metaheuristic optimization and machine learning. IEEE Access. 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3390008.","DOI":"10.1109\/ACCESS.2024.3390008"},{"key":"2822_CR32","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.neucom.2018.06.076","volume":"335","author":"F Zou","year":"2019","unstructured":"Zou F, Chen D, Xu Q. A survey of teaching\u2013learning-based optimization. Neurocomputing. 2019;335:366\u201383.","journal-title":"Neurocomputing"},{"key":"2822_CR33","doi-asserted-by":"publisher","first-page":"186181","DOI":"10.1109\/ACCESS.2020.3029211","volume":"8","author":"AI Kalyakulina","year":"2020","unstructured":"Kalyakulina AI, et al. Ludb: a new open-access validation tool for electrocardiogram delineation algorithms. IEEE Access. 2020;8:186181\u201390.","journal-title":"IEEE Access"},{"key":"2822_CR34","first-page":"673","volume":"1997","author":"P Laguna","year":"1997","unstructured":"Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol. 1997;1997:673\u20136.","journal-title":"Comput Cardiol"},{"issue":"7","key":"2822_CR35","doi-asserted-by":"publisher","first-page":"e17974","DOI":"10.1016\/j.heliyon.2023.e17974","volume":"9","author":"MF Issa","year":"2023","unstructured":"Issa MF, Yousry A, Tuboly G, Juhasz Z, AbuEl-Atta AH, Selim MM. Heartbeat classification based on single lead-II ECG using deep learning. Heliyon. 2023;9(7):e17974.","journal-title":"Heliyon"},{"key":"2822_CR36","doi-asserted-by":"publisher","first-page":"92600","DOI":"10.1109\/ACCESS.2021.3092631","volume":"9","author":"S Nurmaini","year":"2021","unstructured":"Nurmaini S, et al. Beat-to-Beat electrocardiogram waveform classification based on a stacked convolutional and bidirectional long short-term memory. IEEE Access. 2021;9:92600\u201313. https:\/\/doi.org\/10.1109\/ACCESS.2021.3092631.","journal-title":"IEEE Access"},{"key":"2822_CR37","doi-asserted-by":"publisher","unstructured":"A Subasi, SM Qaisar. Heartbeat classification using parametric and time\u2013frequency methods, in Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 2, in 2053\u20132563. , IOP Publishing, 2020:11\u201329. https:\/\/doi.org\/10.1088\/978-0-7503-3411-2ch11.","DOI":"10.1088\/978-0-7503-3411-2ch11"},{"key":"2822_CR38","doi-asserted-by":"crossref","unstructured":"Whitaker BM, Rizwan M, Aydemir VB, Rehg JM, Anderson DV. \u201cAF classification from ECG recording using feature ensemble and sparse coding\u201d, in Computing in Cardiology (CinC). 2017;2017:1\u20134.","DOI":"10.22489\/CinC.2017.174-192"},{"key":"2822_CR39","unstructured":"S. Kutscher. Algorithms for ECG feature extraction: an overview. 2013."},{"issue":"4","key":"2822_CR40","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1080\/03091902.2018.1492039","volume":"42","author":"S Chandra","year":"2018","unstructured":"Chandra S, Sharma A, Singh GK. Feature extraction of ECG signal. J Med Eng Technol. 2018;42(4):306\u201316.","journal-title":"J Med Eng Technol"},{"key":"2822_CR41","doi-asserted-by":"publisher","first-page":"102310","DOI":"10.1016\/j.bspc.2020.102310","volume":"65","author":"A Parsi","year":"2021","unstructured":"Parsi A, Byrne D, Glavin M, Jones E. Heart rate variability feature selection method for automated prediction of sudden cardiac death. Biomed Signal Process Control. 2021;65:102310.","journal-title":"Biomed Signal Process Control"},{"issue":"14","key":"2822_CR42","doi-asserted-by":"publisher","first-page":"19543","DOI":"10.1007\/s11042-021-11492-w","volume":"81","author":"PM Tripathi","year":"2022","unstructured":"Tripathi PM, Kumar A, Komaragiri R, Kumar M. Watermarking of ECG signals compressed using Fourier decomposition method. Multimed Tools Appl. 2022;81(14):19543\u201357.","journal-title":"Multimed Tools Appl"},{"key":"2822_CR43","doi-asserted-by":"crossref","unstructured":"RM Rangayyan, S Krishnan. Biomedical signal analysis.\u00a0Hoboken: Wiley; 2024.","DOI":"10.1002\/9781119825883"},{"key":"2822_CR44","doi-asserted-by":"crossref","unstructured":"R Abeysekera, B Boashash. Time-frequency domain features of ECG signals: their application in P wave detection using the cross Wigner-Ville distribution, in International Conference on Acoustics, Speech, and Signal Processing, 1989:1524\u20131527.","DOI":"10.1109\/ICASSP.1989.266731"},{"key":"2822_CR45","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.dsp.2017.11.003","volume":"77","author":"C Mateo","year":"2018","unstructured":"Mateo C, Talavera JA. Short-time Fourier transform with the window size fixed in the frequency domain. Digit Signal Process. 2018;77:13\u201321.","journal-title":"Digit Signal Process"},{"issue":"2","key":"2822_CR46","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1109\/51.376752","volume":"14","author":"VX Afonso","year":"1995","unstructured":"Afonso VX, Tompkins WJ. Detecting ventricular fibrillation. IEEE Eng Med Biol Mag. 1995;14(2):152\u20139.","journal-title":"IEEE Eng Med Biol Mag"},{"key":"2822_CR47","doi-asserted-by":"crossref","unstructured":"A Mumuni, F Mumuni. Automated data processing and feature engineering for deep learning and big data applications: a survey. J Inf Intell. 2024;1-41.","DOI":"10.1016\/j.jiixd.2024.01.002"},{"issue":"1","key":"2822_CR48","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/electronics9010135","volume":"9","author":"S Nurmaini","year":"2020","unstructured":"Nurmaini S, et al. Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification. Electronics. 2020;9(1):135. https:\/\/doi.org\/10.3390\/electronics9010135.","journal-title":"Electronics"},{"key":"2822_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01571-1","volume":"1","author":"B Tutuko","year":"2021","unstructured":"Tutuko B, et al. AFibNet: an implementation of atrial fibrillation detection with convolutional neural network. BMC Med Inform Decis Mak. 2021;1:1\u201317. https:\/\/doi.org\/10.1186\/s12911-021-01571-1.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"2822_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2011.08.006","volume":"183","author":"RV Rao","year":"2012","unstructured":"Rao RV, Savsani VJ, Vakharia DP. Teaching\u2013learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci (Ny). 2012;183(1):1\u201315.","journal-title":"Inf Sci (Ny)"},{"key":"2822_CR51","unstructured":"HT Ibrahim, WJ Mazher, ON U\u00e7an, O Bayat. Feature selection using salp swarm algorithm for real biomedical datasets. 2017."},{"issue":"13","key":"2822_CR52","doi-asserted-by":"publisher","first-page":"8931","DOI":"10.1007\/s00500-023-08414-3","volume":"27","author":"AC Cinar","year":"2023","unstructured":"Cinar AC. A comprehensive comparison of accuracy-based fitness functions of metaheuristics for feature selection. Soft Comput. 2023;27(13):8931\u201358. https:\/\/doi.org\/10.1007\/s00500-023-08414-3.","journal-title":"Soft Comput"},{"key":"2822_CR53","doi-asserted-by":"publisher","first-page":"104224","DOI":"10.1016\/j.bspc.2022.104224","volume":"79","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Yi J, Chen A, Cheng L. Cardiac arrhythmia classification by time\u2013frequency features inputted to the designed convolutional neural networks. Biomed Signal Process Control. 2023;79:104224.","journal-title":"Biomed Signal Process Control"},{"key":"2822_CR54","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/JTEHM.2022.3232791","volume":"11","author":"B Wang","year":"2022","unstructured":"Wang B, et al. Arrhythmia disease diagnosis based on ECG time\u2013frequency domain fusion and convolutional neural network. IEEE J Transl Eng Heal Med. 2022;11:116\u201325.","journal-title":"IEEE J Transl Eng Heal Med"},{"issue":"9","key":"2822_CR55","doi-asserted-by":"publisher","first-page":"e0274225","DOI":"10.1371\/journal.pone.0274225","volume":"17","author":"MA Kumar","year":"2022","unstructured":"Kumar MA, Chakrapani A. Classification of ECG signal using FFT based improved Alexnet classifier. PLoS One. 2022;17(9):e0274225.","journal-title":"PLoS One"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02822-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02822-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02822-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T12:03:20Z","timestamp":1735560200000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02822-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,30]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2822"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02822-7","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,30]]},"assertion":[{"value":"7 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"412"}}