Heart & lung : the journal of critical care · 2025
Machine learning-based return-to-work assessment system for acute myocardial infarction patients within 12 months.
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Wu Xiaojun, Wang Shiyu, Cui Haoning, Zheng Xianghui, Hou Xinyu, Wang Zhuozhong, Li Qifeng, Liu Qi, Cao Tianhui, Zheng Yang, Wu Jian, Yu Bo
Аннотация
BACKGROUND: Returning to work is a critical indicator of recovery after acute myocardial infarction (AMI), and accurate identification of patients with low return-to-work rates is critical for timely intervention.
OBJECTIVES: To develop a machine learning (ML) model for predicting the return-to-work in AMI patients.
METHODS: A retrospective study of data from 539 AMI patients was conducted using the Incidence Rate of Heart Failure After Acute Myocardial Infarction With Optimal Treatment database. Patients were randomly divided into training cohort and validation cohort (7:3). Seven ML algorithms were used to establish a prediction model for the training cohort. Model performance is evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, F1 score, and Brier score.
RESULTS: This study included 539 AMI patients (median [IQR] age, 50.0 [45.0, 54.0] years; 505 (93.7 %) were male, and 431 (80.0 %) returned to work within one year after discharge. The best-performing model was eXtreme gradient boosting, which achieved an AUC of 0.821 (95 % CI, 0.736-0.907), an accuracy of 0.802 (95 % CI, 0.733-0.861), and an F1 score of 0.873. The return-to-work score and stratification established based on this model can effectively distinguish patients into low, medium, and high probability groups (33.3 % vs. 60.0 % vs. 91.7 %, P < 0.001). The model was deployed on an open website https://amirtw.streamlit.app/, providing a convenient evaluation and analysis tool for medical staff.
CONCLUSION: A new return-to-work ML model was developed, which may help identify patients with low return-to-work rates and may become an effective management tool for AMI patients.
OBJECTIVES: To develop a machine learning (ML) model for predicting the return-to-work in AMI patients.
METHODS: A retrospective study of data from 539 AMI patients was conducted using the Incidence Rate of Heart Failure After Acute Myocardial Infarction With Optimal Treatment database. Patients were randomly divided into training cohort and validation cohort (7:3). Seven ML algorithms were used to establish a prediction model for the training cohort. Model performance is evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, F1 score, and Brier score.
RESULTS: This study included 539 AMI patients (median [IQR] age, 50.0 [45.0, 54.0] years; 505 (93.7 %) were male, and 431 (80.0 %) returned to work within one year after discharge. The best-performing model was eXtreme gradient boosting, which achieved an AUC of 0.821 (95 % CI, 0.736-0.907), an accuracy of 0.802 (95 % CI, 0.733-0.861), and an F1 score of 0.873. The return-to-work score and stratification established based on this model can effectively distinguish patients into low, medium, and high probability groups (33.3 % vs. 60.0 % vs. 91.7 %, P < 0.001). The model was deployed on an open website https://amirtw.streamlit.app/, providing a convenient evaluation and analysis tool for medical staff.
CONCLUSION: A new return-to-work ML model was developed, which may help identify patients with low return-to-work rates and may become an effective management tool for AMI patients.