Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach.

ABSTRACT:

Background

Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking.

Objective

This study aimed to predict LTC service demands for cancer patients and identify the crucial factors.

Methods

3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases.

Results

Service usage di

SUBMITTER: Chien SC 

PROVIDER: S-EPMC10526410 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature