{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T11:39:54Z","timestamp":1784720394811,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Hamadan University of Medical Science","award":["140308227259"],"award-info":[{"award-number":["140308227259"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03411-6","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T07:34:42Z","timestamp":1772264082000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting the severity of COVID-19 using machine learning methods"],"prefix":"10.1186","volume":"26","author":[{"given":"Mehdi","family":"Ezati","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bahar","family":"Masroor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zohreh","family":"Kahramfar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2871-5748","authenticated-orcid":false,"given":"Roya","family":"Najafi-Vosough","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9976-4734","authenticated-orcid":false,"given":"Fatemeh","family":"Amiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"3411_CR1","unstructured":"WHO. 2019-nCoV\/clinical\/2020.4, https:\/\/www.who.int\/publications-detail\/"},{"issue":"1","key":"3411_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s40249-022-00946-4","volume":"11","author":"Y Xiong","year":"2022","unstructured":"Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L, et al. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty. 2022;11(1):19.","journal-title":"Infect Dis Poverty"},{"issue":"1","key":"3411_CR3","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1186\/s12879-024-09298-w","volume":"24","author":"M Seyedtabib","year":"2024","unstructured":"Seyedtabib M, Najafi-Vosough R, Kamyari N. The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study. BMC Infect Dis. 2024;24(1):411.","journal-title":"BMC Infect Dis"},{"issue":"1","key":"3411_CR4","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s12911-022-01939-x","volume":"22","author":"S Moslehi","year":"2022","unstructured":"Moslehi S, Rabiei N, Soltanian AR, Mamani M. Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran. BMC Med Inf Decis Mak. 2022;22(1):192.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"12","key":"3411_CR5","doi-asserted-by":"publisher","first-page":"1038908","DOI":"10.3389\/fcimb.2022.1038908","volume":"13","author":"S Semiz","year":"2022","unstructured":"Semiz S. COVID19 biomarkers: What did we learn from systematic reviews? Front Cell Infect Microbiol. 2022;13(12):1038908.","journal-title":"Front Cell Infect Microbiol"},{"issue":"7","key":"3411_CR6","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1515\/cclm-2020-0369","volume":"58","author":"BM Henry","year":"2020","unstructured":"Henry BM, De Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med. 2020;58(7):1021\u20138.","journal-title":"Clin Chem Lab Med"},{"issue":"4","key":"3411_CR7","first-page":"419","volume":"21","author":"M Esmailian","year":"2022","unstructured":"Esmailian M, Vakili Z, Nasr-Esfahani M, Heydari F, Masoumi B. D-dimer Levels in Predicting Severity of Infection and Outcome in Patients with COVID-19. Tanaffos. 2022;21(4):419\u201333.","journal-title":"Tanaffos"},{"issue":"1","key":"3411_CR8","doi-asserted-by":"publisher","first-page":"21888","DOI":"10.1038\/s41598-021-01462-5","volume":"11","author":"SR Varikasuvu","year":"2021","unstructured":"Varikasuvu SR, Varshney S, Dutt N, Munikumar M, Asfahan S, Kulkarni PP, Gupta P. D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies. Sci Rep. 2021;11(1):21888.","journal-title":"Sci Rep"},{"key":"3411_CR9","doi-asserted-by":"crossref","unstructured":"Jahan H, Yeasmin T, Roy M. Elevated D-dimer is associated with severity of COVID-19: a systematic review and meta analysis. Med Res Arch.2022;10(7) [online].","DOI":"10.18103\/mra.v10i7.2870"},{"key":"3411_CR10","doi-asserted-by":"publisher","first-page":"323","DOI":"10.2147\/TCRM.S357214","volume":"18","author":"L Baroiu","year":"2022","unstructured":"Baroiu L, Lese AC, Stefanopol IA, Iancu A, Dumitru C, Ciubara AB, Bujoreanu FC, Baroiu N, Ciubara A, Nechifor A, Anghel L, Tatu AL. The Role of D-Dimers in the Initial Evaluation of COVID-19. Ther Clin Risk Manag. 2022;18:323\u201335.","journal-title":"Ther Clin Risk Manag"},{"key":"3411_CR11","doi-asserted-by":"crossref","unstructured":"Jin YH, Cai L, Cheng ZS, Cheng H, Deng T, Fan YP, et al. the Zhongnan Hospital of Wuhan University Novel Coronavirus Management and Research Team, Evidence-Based Medicine Chapter of China International Exchange and Promotive Association for Medical and Health Care (CPAM). a rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil Med Res. 2020;7(1):4.","DOI":"10.1186\/s40779-020-0233-6"},{"issue":"4","key":"3411_CR12","first-page":"e22394","volume":"23","author":"R Castaldo","year":"2021","unstructured":"Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and genomic machine learning method performance for prostate cancer diagnosis: systematic literature review. J Med Int Res. 2021;23(4):e22394.","journal-title":"J Med Int Res"},{"key":"3411_CR13","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s00134-019-05872-y","volume":"46","author":"LM Fleuren","year":"2020","unstructured":"Fleuren LM, Klausch TL, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46:383\u2013400.","journal-title":"Intensive Care Med"},{"issue":"8","key":"3411_CR14","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/s41598-017-18564-8","volume":"11","author":"G Guncar","year":"2018","unstructured":"Guncar G, Kukar M, Notar M, Brvar M, C\u02c7ernelc P, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;11(8):411.","journal-title":"Sci Rep"},{"issue":"8","key":"3411_CR15","doi-asserted-by":"publisher","first-page":"554","DOI":"10.3390\/axioms13080554","volume":"13","author":"WM Cuba","year":"2024","unstructured":"Cuba WM, Huaman Alfaro JC, Iftikhar H, L\u00f3pez-Gonzales JL. Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique. Axioms. 2024;13(8):554.","journal-title":"Axioms"},{"issue":"13","key":"3411_CR16","doi-asserted-by":"publisher","first-page":"1644729","DOI":"10.3389\/fpubh.2025.1644729","volume":"10","author":"X Xian","year":"2025","unstructured":"Xian X, Wu S, Fu Y, Fan X, Cheng Y, Zeng L, Hou Z, Chen Y. Incidence of acute hemorrhagic conjunctivitis in Chongqing: a forecasting study based on mathematical models. Front Public Health. 2025;10(13):1644729.","journal-title":"Front Public Health"},{"key":"3411_CR17","doi-asserted-by":"publisher","first-page":"205520762312047","DOI":"10.1177\/20552076231204748","volume":"9","author":"H Iftikhar","year":"2023","unstructured":"Iftikhar H, Daniyal M, Qureshi M, Tawaiah K, Ansah RK, Afriyie JK. A hybrid forecasting technique for infection and death from the mpox virus. Digit Health. 2023;9:20552076231204748.","journal-title":"Digit Health"},{"issue":"7","key":"3411_CR18","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.3390\/diagnostics13071310","volume":"13","author":"HM Alshanbari","year":"2023","unstructured":"Alshanbari HM, Iftikhar H, Khan F, Rind M, Ahmad Z, El-Bagoury AA-AH. On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events. Diagnostics. 2023;13(7):1310.","journal-title":"Diagnostics"},{"key":"3411_CR19","doi-asserted-by":"crossref","unstructured":"Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J. 2020;56(2).","DOI":"10.1183\/13993003.01104-2020"},{"issue":"4","key":"3411_CR20","doi-asserted-by":"publisher","first-page":"e25884","DOI":"10.2196\/25884","volume":"9","author":"S Aktar","year":"2021","unstructured":"Aktar S, Ahamad MM, Rashed-Al-Mahfuz M, Azad A, Uddin S, Kamal A, et al. Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med Inf. 2021;9(4):e25884.","journal-title":"JMIR Med Inf"},{"issue":"1","key":"3411_CR21","first-page":"863","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Francisco Herrera F, Chawla NV. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Arti Intell Res. 2018;61(1):863\u2013905.","journal-title":"J Arti Intell Res"},{"issue":"8","key":"3411_CR22","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1111\/jth.14939","volume":"18","author":"J Thachil","year":"2020","unstructured":"Thachil J. All those D-dimers in COVID\u201019. J Thromb Haemost. 2020;18(8):2075\u20136.","journal-title":"J Thromb Haemost"},{"issue":"478","key":"3411_CR23","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1198\/016214507000000149","volume":"102","author":"H Uno","year":"2007","unstructured":"Uno H, Cai T, Tian L, Wei L-J. Evaluating prediction rules for t-year survivors with censored regression models. J Ame Stat Assoc. 2007;102(478):527\u201337.","journal-title":"J Ame Stat Assoc"},{"key":"3411_CR24","doi-asserted-by":"publisher","first-page":"104268","DOI":"10.1016\/j.ijmedinf.2020.104268","volume":"143","author":"K De Silva","year":"2020","unstructured":"De Silva K, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. Int J Med Inf. 2020;143:104268.","journal-title":"Int J Med Inf"},{"key":"3411_CR25","first-page":"100024","volume":"4","author":"SK Kiangala","year":"2021","unstructured":"Kiangala SK, Wang Z. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment. Mach Learn App. 2021;4:100024.","journal-title":"Mach Learn App"},{"issue":"1","key":"3411_CR26","doi-asserted-by":"publisher","first-page":"2150","DOI":"10.1186\/s12889-025-23318-7","volume":"10","author":"MD Teja","year":"2025","unstructured":"Teja MD, Rayalu GM. Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis. BMC Public Health. 2025;10(1):2150.","journal-title":"BMC Public Health"},{"issue":"5","key":"3411_CR27","first-page":"761","volume":"10","author":"D Dobrijevi\u0107","year":"2023","unstructured":"Dobrijevi\u0107 D, Anti\u0107 J, Raki\u0107 G, Katani\u0107 J, Andrijevi\u0107 L, Pastor K. Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach. Child (Basel). 2023;10(5):761.","journal-title":"Child (Basel)"},{"key":"3411_CR28","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. \u2013 XGBoost: A Scalable Tree Boosting SystemKDD, (2016) KDD \u201816: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"3411_CR29","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1111\/jcmm.17098","volume":"26","author":"PG Asteris","year":"2022","unstructured":"Asteris PG, Gavriilaki E, Touloumenidou T, Koravou EE, Koutra M, Papayanni PG, et al. Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks. J Cell Mol Med. 2022;26(5):1445\u201355.","journal-title":"J Cell Mol Med"},{"key":"3411_CR30","doi-asserted-by":"publisher","first-page":"109218","DOI":"10.1016\/j.clim.2022.109218","volume":"246","author":"PG Asteris","year":"2023","unstructured":"Asteris PG, Kokoris S, Gavriilaki E, Tsoukalas MZ, Houpas P, Paneta M, et al. Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices. Clin Immunol. 2023;246:109218.","journal-title":"Clin Immunol"},{"issue":"1","key":"3411_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40560-020-00466-z","volume":"8","author":"Y Yao","year":"2020","unstructured":"Yao Y, Cao J, Wang Q, Shi Q, Liu K, Luo Z, et al. D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study. J intensive care. 2020;8(1):1\u201311.","journal-title":"J intensive care"},{"issue":"6","key":"3411_CR32","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1111\/jth.14859","volume":"18","author":"L Zhang","year":"2020","unstructured":"Zhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, et al. D-dimer levels on admission to predict in\u2010hospital mortality in patients with Covid\u201019. J Thromb Haemost. 2020;18(6):1324\u20139.","journal-title":"J Thromb Haemost"},{"issue":"1","key":"3411_CR33","doi-asserted-by":"publisher","first-page":"10738","DOI":"10.1038\/s41598-021-90265-9","volume":"11","author":"M Kukar","year":"2021","unstructured":"Kukar M, Gun\u010dar G, Vovko T, Podnar S, \u010cernel\u010d P, Brvar M, et al. COVID-19 diagnosis by routine blood tests using machine learning. Sci Rep. 2021;11(1):10738.","journal-title":"Sci Rep"},{"issue":"1","key":"3411_CR34","doi-asserted-by":"publisher","first-page":"235349","DOI":"10.1080\/21645515.2024.2353491","volume":"20","author":"X Yang","year":"2024","unstructured":"Yang X, Shi F, Zhang J, Gao H, Chen S, Olatosi B, et al. Vaccination status and disease severity of COVID-19 in different phases of the pandemic. Hum Vaccin Immunother. 2024;20(1):235349.","journal-title":"Hum Vaccin Immunother"},{"key":"3411_CR35","doi-asserted-by":"publisher","first-page":"e168","DOI":"10.1017\/S0950268822001418","volume":"150","author":"C Magazzino","year":"2022","unstructured":"Magazzino C, Mele M, Coccia M. A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality. Epidemiol Infect. 2022;150:e168.","journal-title":"Epidemiol Infect"},{"key":"3411_CR36","doi-asserted-by":"crossref","unstructured":"Foy BH, Carlson JCT, Reinertsen E, Valls PIR, Pallares Lopez R, Palanques-Tost E, et al. Association of red blood cell distribution width with mortality risk in hospitalized adults with SARS-CoV-2 infection. JAMA Netw Open. 2020 1;3(9):e2022058.","DOI":"10.1001\/jamanetworkopen.2020.22058"},{"key":"3411_CR37","doi-asserted-by":"publisher","first-page":"681608","DOI":"10.3389\/fdgth.2021.681608","volume":"3","author":"E Jamshidi","year":"2022","unstructured":"Jamshidi E, Asgary A, Tavakoli N, Zali A, Setareh S, Esmaily H, Jamaldini SH, Daaee A, Babajani A, Sendani Kashi MA, Jamshidi M, Jamal Rahi S, Mansouri N. Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU. Front Digit Health. 2022;3:681608.","journal-title":"Front Digit Health"},{"issue":"4","key":"3411_CR38","first-page":"82","volume":"9","author":"A Suruliandi","year":"2025","unstructured":"Suruliandi A, Ame Rayan R, Raja SP. Prediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches. Int J Interact Multi Arti Intell. 2025;9(4):82\u201398.","journal-title":"Int J Interact Multi Arti Intell"},{"issue":"11","key":"3411_CR39","doi-asserted-by":"publisher","first-page":"e21983","DOI":"10.1016\/j.heliyon.2023.e21983","volume":"9","author":"NG Beceren","year":"2023","unstructured":"Beceren NG, Arma\u011fan HH, O\u011fuzlar F\u00c7, Cesur E, G\u00fcrdal O, Tomruk \u00d6. Can mean platelet volume be a prognosis predictor in viral infections: An example of Covid-19. Heliyon. 2023;9(11):e21983.","journal-title":"Heliyon"},{"issue":"2","key":"3411_CR40","doi-asserted-by":"publisher","first-page":"e0316467","DOI":"10.1371\/journal.pone.0316467","volume":"20","author":"A Safdari","year":"2025","unstructured":"Safdari A, Keshav CS, Mody D, Verma K, Kaushal U, Burra VK, Ray S, Bandyopadhyay D. The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe. PLoS ONE. 2025;20(2):e0316467.","journal-title":"PLoS ONE"},{"key":"3411_CR41","doi-asserted-by":"publisher","first-page":"6606842","DOI":"10.1155\/cjid\/6606842","volume":"2025","author":"R Dong","year":"2025","unstructured":"Dong R, Yao H, Chen T, Yang W, Zhou Q, Xu J. Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers. Can J Infect Dis Med Microbiol. 2025;2025:6606842.","journal-title":"Can J Infect Dis Med Microbiol"},{"key":"3411_CR42","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ejim.2024.02.037","volume":"125","author":"PG Asteris","year":"2024","unstructured":"Asteris PG, Gandomi AH, Armaghani DJ, Kokoris S, Papandreadi AT, Roumelioti A, et al. Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm. Eur J Intern Med. 2024;125:67\u201373.","journal-title":"Eur J Intern Med"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03411-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03411-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03411-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T12:27:19Z","timestamp":1775564839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03411-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3411"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03411-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]},"assertion":[{"value":"25 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted after official approval by the ethics committee at Hamadan University of Medical Sciences (Ethics code: IR.UMSHA.REC.1403.591). If the patient was hospitalized at the time of data collection, the participants or their legal guardians provided informed consent. In some situation, e.g. discharge from the hospital, the requirement for completing the consent form was waived due to the retrospective nature of the study, but with approving by the ethics committee. All procedures were carried out in accordance with local regulations governing research involving human subjects and the ethical principles outlined in the Declaration of Helsinki.","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":"112"}}