Digital health · 2026
Evaluation of symptom-management medications for predicting short-term survival in advanced cancer patients with machine learning.
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Hsu Hua-Shui, Kao Chia-Hung, Chang Shih-Sheng, Wu Kuo-Chen, Huang Po-Tsung, Tsai Shen-Ju, Tang Ya-Zhu, Lin Wen-Yuan
Аннотация
OBJECTIVE: Estimating the diverse symptoms of patients with advanced cancer is helpful for young physicians and medical teams in planning appropriate palliative care. We evaluated the use of medication, comorbidities, laboratory test results, and vital signs in hospitalized patients to predict death within 14 days.
METHODS: We retrospectively selected hospitalized patients with advanced cancer who were admitted to the hospice ward. We are using extreme gradient boosting (XGBoost) and a combination of random forest (RF) and XGBoost (RF-XGBoost) models to analyze sixteen comorbidities, eighteen types of medications, twenty-six laboratory tests, and six vital signs. Finally, SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contribution of each feature to survival prediction.
RESULTS: Among the 2276 patients, 73% survived less than 14 days. The Area under the curve (AUC) of the XGBoost and RF-XGBoost models was 0.82 and 0.81 (P < 0.001), respectively. Among the top 10 most important feature values of both machine learning models after SHAP analysis, seven were related to medication use, whereas three were related to laboratory tests. The top three ranked feature values were stool softeners, antiemetics and sedatives. Patients who received these medications generally had a strong positive correlation with survival beyond 14 days.
CONCLUSIONS: Our results suggest that the types of medications used by patients, especially stool softeners, antiemetics, and sedatives, are valuable in predicting survival beyond 14 days for hospitalized patients with advanced cancer. This result may assist young physicians and medical teams in developing appropriate palliative care plans for patients and their families.
METHODS: We retrospectively selected hospitalized patients with advanced cancer who were admitted to the hospice ward. We are using extreme gradient boosting (XGBoost) and a combination of random forest (RF) and XGBoost (RF-XGBoost) models to analyze sixteen comorbidities, eighteen types of medications, twenty-six laboratory tests, and six vital signs. Finally, SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contribution of each feature to survival prediction.
RESULTS: Among the 2276 patients, 73% survived less than 14 days. The Area under the curve (AUC) of the XGBoost and RF-XGBoost models was 0.82 and 0.81 (P < 0.001), respectively. Among the top 10 most important feature values of both machine learning models after SHAP analysis, seven were related to medication use, whereas three were related to laboratory tests. The top three ranked feature values were stool softeners, antiemetics and sedatives. Patients who received these medications generally had a strong positive correlation with survival beyond 14 days.
CONCLUSIONS: Our results suggest that the types of medications used by patients, especially stool softeners, antiemetics, and sedatives, are valuable in predicting survival beyond 14 days for hospitalized patients with advanced cancer. This result may assist young physicians and medical teams in developing appropriate palliative care plans for patients and their families.