Life and Functional Time Prediction Using Machine Learning in Palliative Care

Music City Center 

To predict exact life expectancy is needed to plan patient's future in palliative care. The aim of this study is to apply multiple machine learning models to achieve highly accurate prediction and to consider factors that influence functional and life prognosis. Three types of functional time predictions for walking, eating, and communicating, and life time prediction was analyzed. Functional and life time prediction were analyzed using four models: decision tree, LASSO regression, random forest, and XGBoost. None of the models achieved high accuracy in each prediction. The feature importance of each model showed different characteristics when comparing each prediction and model. RMSE of LASSO regression, random forest, and XGBoost were about 7 days for each functional time prediction and about 6 days for life time prediction. In this study, the survival period was limited to 30 days or less, so this error is considered to be very large for patients. The feature importance showed that laboratory data was important for each prediction. In the prediction using machine learning, not all models achieved high accuracy. However, very useful results were obtained from feature importance.

palliative care

machine learning

decision tree

LASSO

random forest

XGBoost 

Section on Statistics in Epidemiology