Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach
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 progno...
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MDPI AG
2022-01-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/1/32 |
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| author | Che-Cheng Chang Jiann-Horng Yeh Hou-Chang Chiu Yen-Ming Chen Mao-Jhen Jhou Tzu-Chi Liu Chi-Jie Lu |
| author_facet | Che-Cheng Chang Jiann-Horng Yeh Hou-Chang Chiu Yen-Ming Chen Mao-Jhen Jhou Tzu-Chi Liu Chi-Jie Lu |
| author_sort | Che-Cheng Chang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:10:05Z |
| format | Article |
| id | doaj.art-31b024828c2544c7b3a7bec3709b9164 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T01:10:05Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-31b024828c2544c7b3a7bec3709b91642023-11-23T14:19:26ZengMDPI AGJournal of Personalized Medicine2075-44262022-01-011213210.3390/jpm12010032Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based ApproachChe-Cheng Chang0Jiann-Horng Yeh1Hou-Chang Chiu2Yen-Ming Chen3Mao-Jhen Jhou4Tzu-Chi Liu5Chi-Jie Lu6Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, TaiwanSchool of Medicine, Fu Jen Catholic University, New Taipei City 24205, TaiwanSchool of Medicine, Fu Jen Catholic University, New Taipei City 24205, TaiwanDepartment of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, TaiwanGraduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, TaiwanDepartment of Business Administration, Fu Jen Catholic University, New Taipei City, 242062, TaiwanGraduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, TaiwanMyasthenia 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.https://www.mdpi.com/2075-4426/12/1/32myasthenia gravismachine learningintensive care unitdecision treepredication |
| spellingShingle | Che-Cheng Chang Jiann-Horng Yeh Hou-Chang Chiu Yen-Ming Chen Mao-Jhen Jhou Tzu-Chi Liu Chi-Jie Lu Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach Journal of Personalized Medicine myasthenia gravis machine learning intensive care unit decision tree predication |
| title | Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach |
| title_full | Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach |
| title_fullStr | Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach |
| title_full_unstemmed | Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach |
| title_short | Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach |
| title_sort | utilization of decision tree algorithms for supporting the prediction of intensive care unit admission of myasthenia gravis a machine learning based approach |
| topic | myasthenia gravis machine learning intensive care unit decision tree predication |
| url | https://www.mdpi.com/2075-4426/12/1/32 |
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