{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T11:30:38Z","timestamp":1782473438602,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National High Level Hospital Clinical Research Funding","award":["2022-GSP-GG-25"],"award-info":[{"award-number":["2022-GSP-GG-25"]}]},{"name":"National High Level Hospital Clinical Research Funding","award":["2021-I2M-1-056"],"award-info":[{"award-number":["2021-I2M-1-056"]}]},{"name":"CAMS Innovation Fund for Medical Sciences (CIFMS)","award":["2022-GSP-GG-25"],"award-info":[{"award-number":["2022-GSP-GG-25"]}]},{"name":"CAMS Innovation Fund for Medical Sciences (CIFMS)","award":["2021-I2M-1-056"],"award-info":[{"award-number":["2021-I2M-1-056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Ventricular tachycardia (VT) can broadly be categorised into ischemic heart disease, non-ischemic structural heart disease, and idiopathic VT. There are few studies related to the application of machine learning for the etiological diagnosis of VT, and the interpretable methods are still in the exploratory stage for clinical decision-making applications. Objectives: The aim is to propose a machine learning model for the etiological diagnosis of VT. Interpretable results based on models are compared with expert knowledge, and interpretable evaluation protocols for clinical decision-making applications are developed. Methods: A total of 1305 VT patient data from 1 January 2013 to 1 September 2023 at the Arrhythmia Centre of Fuwai Hospital were included in the study. Clinical data collected during hospitalisation included demographics, medical history, vital signs, echocardiographic results, and laboratory test outcomes. Results: The XGBoost model demonstrated the best performance in VT etiological diagnosis (precision, recall, and F1 were 88.4%, 88.5%, and 88.4%, respectively). A total of four interpretable machine learning methods applicable to clinical decision-making were evaluated in terms of visualisation, clinical usability, clinical applicability, and efficiency with expert knowledge interpretation. Conclusions: The XGBoost model demonstrated superior performance in the etiological diagnosis of VT, and SHAP and decision tree interpretable methods are more favoured by clinicians for decision-making.<\/jats:p>","DOI":"10.3390\/diagnostics14202291","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T04:45:35Z","timestamp":1729485935000},"page":"2291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8003-8124","authenticated-orcid":false,"given":"Min","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Hu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College\/National Center for Cardiovascular Diseases, Fuwai Hospital, Beijing 100037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-9590","authenticated-orcid":false,"given":"Ziyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9834-9774","authenticated-orcid":false,"given":"Si","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Yao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College\/National Center for Cardiovascular Diseases, Fuwai Hospital, Beijing 100037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/nrcardio.2011.15","article-title":"The diagnosis and management of ventricular arrhythmias","volume":"8","author":"Lau","year":"2011","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.jacc.2009.06.016","article-title":"Incidence of Sudden Cardiac Death in China","volume":"54","author":"Hua","year":"2009","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1093\/eurheartj\/ehac262","article-title":"2022 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death","volume":"43","author":"Zeppenfeld","year":"2022","journal-title":"Eur. Heart J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e247","DOI":"10.1016\/j.jacc.2006.07.010","article-title":"ACC\/AHA\/ESC 2006 Guidelines for Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death","volume":"48","author":"Zipes","year":"2006","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_5","first-page":"e91","article-title":"2017 AHA\/ACC\/HRS Guideline for Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death","volume":"72","author":"Stevenson","year":"2018","journal-title":"Circulation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1093\/europace\/euu194","article-title":"EHRA\/HRS\/APHRS expert consensus on ventricular arrhythmias","volume":"16","author":"Pedersen","year":"2014","journal-title":"Europace"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1161\/CIRCEP.115.003080","article-title":"Catheter Ablation of Fascicular Ventricular Tachycardia","volume":"8","author":"Liu","year":"2015","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e20220411","DOI":"10.36660\/abc.20220411","article-title":"Resson\u00e2ncia Magn\u00e9tica Card\u00edaca como Ferramenta Diagn\u00f3stica Etiol\u00f3gica em Pacientes Recuperados de Morte S\u00fabita Card\u00edaca ou Arritmias Ventriculares Inst\u00e1veis","volume":"120","author":"Wada","year":"2023","journal-title":"Arq. Bras. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e55802","DOI":"10.2196\/55802","article-title":"Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis within the Frameworks of Knowledge and Practice: Survey Study","volume":"11","author":"Hu","year":"2024","journal-title":"JMIR Hum. Factors"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Oliva, G., Hak, F., Guimar\u00e3es, T., and Santos, M. (2022). Towards effective clinical decision support systems: A systematic review. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0272846"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Akbulut, S., Yagin, F.H., Cicek, I.B., Koc, C., Colak, C., and Yilmaz, S. (2023). Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence. Diagnostics, 13.","DOI":"10.3390\/diagnostics13061173"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ayano, Y.M., Schwenker, F., Dufera, B.D., and Debelee, T.G. (2022). Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010111"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"H1337","DOI":"10.1152\/ajpheart.00764.2020","article-title":"Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation","volume":"320","author":"Atienza","year":"2021","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R.T., Mielke, J., Gill, S., Kotecha, D., Freitag, D.F., and Denaxas, S. (2021). Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: Systematic review of validity and clinical utility. BMC Med., 19.","DOI":"10.1186\/s12916-021-01940-7"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e26964","DOI":"10.2196\/26964","article-title":"Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study","volume":"8","author":"Matthiesen","year":"2021","journal-title":"JMIR Hum. Factors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yagin, B., Yagin, F., Colak, C., Inceoglu, F., Kadry, S., and Kim, J. (2023). Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research. Diagnostics, 13.","DOI":"10.3390\/diagnostics13213314"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.cjca.2021.09.004","article-title":"Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology","volume":"38","author":"Petch","year":"2022","journal-title":"Can. J. Cardiol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sheu, R.-K., and Pardeshi, M.S. (2022). A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System. Sensors, 22.","DOI":"10.3390\/s22208068"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vaquerizo-Villar, F., Guti\u00e9rrez-Tobal, G.C., Calvo, E., \u00c1lvarez, D., Kheirandish-Gozal, L., del Campo, F., Gozal, D., and Hornero, R. (2023). An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea. Comput. Biol. Med., 165.","DOI":"10.1016\/j.compbiomed.2023.107419"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Gavald\u00e0, R., and Baixeries, J. (2022). Interpretable prediction of mortality in liver transplant recipients based on machine learning. Comput. Biol. Med., 151.","DOI":"10.1016\/j.compbiomed.2022.106188"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Mach","year":"2001","journal-title":"Mach Learn."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-KDD, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_24","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 4\u20139). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, New York, NY, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_26","unstructured":"Trevor, H., Robert, T., and Jerome, H.F. (2009). The Elements of Statistical Learning, Springer."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1080\/10618600.2014.907095","article-title":"Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation","volume":"24","year":"2015","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_28","unstructured":"Marco Tulio Ribeiro, S.S., and Guestrin, C. (2016). Model-agnostic interpretability of machine learning. arXiv."},{"key":"ref_29","unstructured":"EUCA (2024, May 13). The End-User-Centered Explainable AI Framework. Available online: https:\/\/weina.me\/end-user-xai\/paper\/EndUserXAI_manuscript.pdf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3852","DOI":"10.1038\/s41467-020-17431-x","article-title":"Explainable artificial intelligence model to predict acute critical illness from electronic health records","volume":"11","author":"Lauritsen","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102684","DOI":"10.1016\/j.media.2022.102684","article-title":"Guidelines and evaluation of clinical explainable AI in medical image analysis","volume":"84","author":"Jin","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Elmannai, H., El-Rashidy, N., Mashal, I., Alohali, M.A., Farag, S., El-Sappagh, S., and Saleh, H. (2023). Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence. Diagnostics, 13.","DOI":"10.3390\/diagnostics13081506"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1161\/CIRCULATIONAHA.118.031734","article-title":"From Machine Learning to Artificial Intelligence Applications in Cardiac Care","volume":"138","author":"Tsay","year":"2018","journal-title":"Circulation"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"20220317","DOI":"10.1098\/rsif.2022.0317","article-title":"From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy","volume":"19","author":"Buck","year":"2022","journal-title":"J. R. Soc. Interface"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1093\/eurheartj\/eht439","article-title":"A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD)","volume":"35","author":"Jichi","year":"2014","journal-title":"Eur. Heart J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"euad181","DOI":"10.1093\/europace\/euad181","article-title":"Bayesian analysis of the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia trial","volume":"25","author":"Avila","year":"2023","journal-title":"Europace"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.reth.2024.03.001","article-title":"Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model","volume":"27","author":"Sadeghian","year":"2024","journal-title":"Regen. Ther."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.jcmg.2018.06.030","article-title":"Machine Learning for Data-Driven Discovery","volume":"12","author":"Sengupta","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.repc.2012.10.020","article-title":"A resson\u00e2ncia magn\u00e9tica card\u00edaca como uma mais-valia no diagn\u00f3stico etiol\u00f3gico de arritmias ventriculares","volume":"32","author":"Cabanelas","year":"2013","journal-title":"Rev. Port. Cardiol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Antoniadi, A.M., Galvin, M., Heverin, M., Hardiman, O., and Mooney, C. (2021). Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-91632-2"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alabi, R.O., Elmusrati, M., Leivo, I., Almangush, A., and Makitie, A.A. (2023). Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35795-0"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40708-024-00222-1","article-title":"Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer\u2019s disease detection","volume":"11","author":"Vimbi","year":"2024","journal-title":"Brain Inf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"252","DOI":"10.4258\/hir.2012.18.4.252","article-title":"Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge","volume":"18","year":"2012","journal-title":"Healthc. Inform. Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shoaip, N., El-Sappagh, S., Abuhmed, T., and Elmogy, M. (2024). A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-54065-1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TRPMS.2021.3066428","article-title":"On Interpretability of Artificial Neural Networks: A Survey","volume":"5","author":"Fan","year":"2021","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bietenbeck, A., and Streichert, T. (2021). Preparing Laboratories for Interconnected Health Care. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081487"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"El-Sappagh, S., Alonso, J.M., Islam, S.M.R., Sultan, A.M., and Kwak, K.S. (2021). A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer\u2019s disease. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-82098-3"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mun, C., Ha, H., Lee, O., and Cheon, M. (2024). Enhancing AI-CDSS with U-AnoGAN: Tackling data imbalance. Comput. Methods Programs Biomed., 244.","DOI":"10.1016\/j.cmpb.2023.107954"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2018.17163","article-title":"Clinical Decision Support in the Era of Artificial Intelligence","volume":"320","author":"Shortliffe","year":"2018","journal-title":"JAMA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s41666-022-00114-1","article-title":"Explainable Artificial Intelligence for Predictive Modeling in Healthcare","volume":"6","author":"Yang","year":"2022","journal-title":"J. Healthc. Inform Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Carvalho, D.V., Pereira, E.M., and Cardoso, J.S. (2019). Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8.","DOI":"10.3390\/electronics8080832"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Laxar, D., Eitenberger, M., Maleczek, M., Kaider, A., Hammerle, F.P., and Kimberger, O. (2023). The influence of explainable vs non-explainable clinical decision support systems on rapid triage decisions: A mixed methods study. BMC Med., 21.","DOI":"10.1186\/s12916-023-03068-2"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Casalino, G., Kaymak, U., and Zaza, G. (2021, January 5\u20137). Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA.","DOI":"10.1109\/SSCI50451.2021.9660104"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Smole, T., \u017dunkovi\u010d, B., Pi\u010dulin, M., Kokalj, E., Robnik-\u0160ikonja, M., Kukar, M., Fotiadis, D.I., Pezoulas, V.C., Tachos, N.S., and Barlocco, F. (2021). A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. Comput. Biol. Med., 135.","DOI":"10.1016\/j.compbiomed.2021.104648"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/20\/2291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:02Z","timestamp":1760112842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/20\/2291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":54,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["diagnostics14202291"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14202291","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}