A machine-learning-based prediction of non-home discharge among acute heart failure patients.

ABSTRACT:

Background

Scarce data on factors related to discharge disposition in patients hospitalized for acute heart failure (AHF) were available, and we sought to develop a parsimonious and simple predictive model for non-home discharge via machine learning.

Methods

This observational cohort study using a Japanese national database included 128,068 patients admitted from home for AHF between April 2014 and March 2018. The candidate predictors for non-home discharge were patient demographics, comorbidities, and treatment performed within 2 days after hospital admission. We used 80% of the population to develop a model using all 26 candidate variables and using the variable selected by 1 standard-error rule of Lasso regression, which enhances interpretability, and 20% to validate the

SUBMITTER: Okada A 

PROVIDER: S-EPMC10955024 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature