{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:05:53Z","timestamp":1780365953734,"version":"3.54.1"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Purpose<\/jats:title>\n<jats:p>To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>We collected 13,245 written judgments from <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/PKULAW.COM\" xlink:type=\"simple\">PKULAW.COM<\/jats:ext-link>, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The optimal model of this study is expected to predict the causality accurately.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0300662","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T17:43:15Z","timestamp":1713375795000},"page":"e0300662","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China"],"prefix":"10.1371","volume":"19","author":[{"given":"Qingxin","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangpeng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9889-6465","authenticated-orcid":true,"given":"Hong","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"issue":"6","key":"pone.0300662.ref001","doi-asserted-by":"crossref","first-page":"e116","DOI":"10.1371\/journal.pcbi.0030116","article-title":"Machine learning and its applications to biology","volume":"3","author":"AL Tarca","year":"2007","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pone.0300662.ref002","doi-asserted-by":"crossref","first-page":"5058","DOI":"10.1038\/s41467-023-40797-7","article-title":"APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants","volume":"14","author":"SD Bianco","year":"2023","journal-title":"Nature Communications"},{"issue":"7","key":"pone.0300662.ref003","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1038\/s43588-023-00482-7","article-title":"Neural ADMIXTURE for rapid genomic clustering","volume":"3","author":"A Dominguez Mantes","year":"2023","journal-title":"Nature Computational Science"},{"issue":"1","key":"pone.0300662.ref004","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1038\/s41467-024-44740-2","article-title":"Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning","volume":"15","author":"WJ Bolton","year":"2024","journal-title":"Nature Communications"},{"issue":"7","key":"pone.0300662.ref005","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1038\/s41591-023-02396-3","article-title":"Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction","volume":"29","author":"SS Al-Zaiti","year":"2023","journal-title":"Nature Medicine"},{"issue":"1","key":"pone.0300662.ref006","doi-asserted-by":"crossref","first-page":"11703","DOI":"10.1038\/s41598-022-15691-9","article-title":"Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.","volume":"12","author":"Y-H Lee","year":"2022","journal-title":"Scientific Reports"},{"issue":"2","key":"pone.0300662.ref007","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s00414-022-02928-5","article-title":"Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives","volume":"137","author":"N Galante","year":"2023","journal-title":"Int J Legal Med"},{"key":"pone.0300662.ref008","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.msksp.2018.11.012","article-title":"Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy","volume":"39","author":"C. 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Pr\u00f6pper","year":"2014","journal-title":"Z Gastroenterol."},{"issue":"1","key":"pone.0300662.ref040","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s42836-022-00141-8","article-title":"Litigation analysis of medical damage after total knee arthroplasty: a case study based on Chinese legal database in the past ten years.","volume":"4","author":"S Liu","year":"2022","journal-title":"Arthroplasty."},{"issue":"6","key":"pone.0300662.ref041","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1111\/1556-4029.14089","article-title":"Analysis of Dental Case Reports in the Context of Court Decisions: Causal Nexus and Aspects of Fault","volume":"64","author":"DBL Loreto","year":"2019","journal-title":"J Forensic Sci"},{"key":"pone.0300662.ref042","doi-asserted-by":"crossref","first-page":"103860","DOI":"10.1016\/j.apergo.2022.103860","article-title":"Cognitive bias in workplace investigation: Problems, perspectives and proposed solutions.","volume":"105","author":"CL MacLean","year":"2022","journal-title":"Appl Ergon."},{"issue":"5","key":"pone.0300662.ref043","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1111\/1556-4029.14031","article-title":"How Cross-Examination on Subjectivity and Bias Affects Jurors\u2019 Evaluations of Forensic Science Evidence","volume":"64","author":"WC Thompson","year":"2019","journal-title":"J Forensic Sci"},{"issue":"3","key":"pone.0300662.ref044","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s00414-013-0836-5","article-title":"Malpractice and medical liability. 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Weng","year":"2023","journal-title":"Science and the Law"},{"key":"pone.0300662.ref049","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/978-3-031-41081-9_4","volume-title":"Artificial Intelligence and Normative Challenges: International and Comparative Legal Perspectives","author":"V. Papadouli","year":"2023"},{"key":"pone.0300662.ref050","doi-asserted-by":"crossref","first-page":"1095001","DOI":"10.3389\/fpubh.2022.1095001","article-title":"Artificial intelligence and digital medicine for integrated home care services in Italy: Opportunities and limits","volume":"10","author":"M Cingolani","year":"2022","journal-title":"Front Public Health"},{"issue":"1","key":"pone.0300662.ref051","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1057\/s41599-020-0501-9","article-title":"Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward","volume":"7","author":"S. 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