{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:12:55Z","timestamp":1768590775896,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,2]],"date-time":"2022-01-02T00:00:00Z","timestamp":1641081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.<\/jats:p>","DOI":"10.3390\/jpm12010032","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T01:14:02Z","timestamp":1641518042000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach"],"prefix":"10.3390","volume":"12","author":[{"given":"Che-Cheng","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan"},{"name":"Ph.D. Program in Nutrition and Food Sciences, Human Ecology College, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiann-Horng","family":"Yeh","sequence":"additional","affiliation":[{"name":"School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan"},{"name":"Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan"},{"name":"Department of Neurology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2712-8533","authenticated-orcid":false,"given":"Hou-Chang","family":"Chiu","sequence":"additional","affiliation":[{"name":"School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan"},{"name":"Department of Neurology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mao-Jhen","family":"Jhou","sequence":"additional","affiliation":[{"name":"Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tzu-Chi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Business Administration, Fu Jen Catholic University, New Taipei City, 242062, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7911-2253","authenticated-orcid":false,"given":"Chi-Jie","family":"Lu","sequence":"additional","affiliation":[{"name":"Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan"},{"name":"Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan"},{"name":"Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1056\/NEJMra1602678","article-title":"Myasthenia Gravis","volume":"375","author":"Gilhus","year":"2016","journal-title":"N. 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