Interpretable machine learning approach for optimizing hospice care predictions using health assessment data.

ABSTRACT:

Background

Determining the appropriate end-of-life (EOL) care model within a short time frame is challenging and requires extensive experience. To the best of our knowledge, no studies have developed automatic systems for identifying the hospice care models: hospice home (HHC), inpatient (HIC), and shared care (HSC). This study aimed to determine the optimal hospice care model for EOL patients with machine learning (ML) methods based on health assessment data.

Methods

We employed high-performance ML methods to build prediction models that could predict the most appropriate hospice care service for each patient using their health assessment data. Furthermore, we employed the knowledge distillation technique to transfer knowledge from the best-performing ML model to a decision

SUBMITTER: Cho SY 

PROVIDER: S-EPMC12752430 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature