Survival models and longitudinal medical events for hospital readmission forecasting.

ABSTRACT:

Background

The rate of 30-day all-cause hospital readmissions can affect the funding a hospital receives. An accurate and reliable readmission prediction model could save money and increase quality-of-care. Few projects have explored formulating this task as a survival prediction problem, where models can exploit a real-valued time-to-readmission target. This paper demonstrates the effectiveness of a survival-inspired readmission model, especially when paired with a longitudinal patient representation that is agnostic to disease-cohort and predictive task.

Methods

We forecast readmissions for a population-level cohort of 421,088 patients discharged in 2015 and 2016 from hospitals in Alberta, Canada. Clinical features and sequences of historical medical codes (calculated from

SUBMITTER: Davis S 

PROVIDER: S-EPMC11559171 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature