Conclusions
death, terminal cancer
EVENTS, TERMINAL CANCER
The findings of this study suggest that it is possible to predict death events among patients with terminal cancer using wearable devices and machine learning techniques. Although our prototype demonstrates the potential of these approaches in end-of-life care, further research is needed to confirm the robustness of the models and their effectiveness in real-world settings.The authors would like to thank Zhi-Lei Yu for his help with case management and follow-up assessments. The authors would like to thank Hue-Chun Yeh and Wei-Fang Hsu for their help with referring and assessing home care patients. The authors thank the patients, caregivers, families, health professionals, and all others who participated in the study.This study was supported by the Taiwan Ministry of Science and Technology under the grant MOST 110-2634-F-002-032.Authors' Contributions: J-HL conceived the main idea and developed the study protocol in discussion with FL, S-YC, J-ST, C-YS, H-LH, and J-KP. FL acquired funding from the Taiwan Ministry of Science and Technology and provided the wearable devices and data-collecting platform. J-ST, C-YS, H-LH, and J-KP referred qualified participants to the study. FL, S-YC, and J-ST supervised the study. J-HL performed the data analysis and wrote the manuscript. All other authors provided feedback and helped modify the manuscript.Conflicts of Interest: None declared.Survival prediction tools in end-of-life care.Clinical assessment items (translated to English).Shapley additive explanations value analysis of different models (extreme gradient boosting, deep neural network, random forest, and k-nearest neighbor).Shapley additive explanations value analysis of the wrong prediction cases (extreme gradient boosting).The extreme gradient boosting model’s predictions in different age groups.Using environmental factors in predicting the end-of-life status.