The impact of knowledge of hospitalization on mortality predictions.

ABSTRACT: Despite the rapid advancement of machine learning algorithms, the important problem of distinguishing patients based on the likelihood of their mortality remains a challenge. In this paper, we investigated the degree to which the incorporation of the time-varying factor, length of hospitalization could contribute to modeling mortality. A two-part modeling approach was proposed to capture the potential heterogeneity over follow-up time and to evaluate the extent to which allowing a predictor based on a fixed-time event like hospitalization (as a time-varying coefficient) enhanced mortality prediction. A test was then conducted to assess whether the association between hospitalization and mortality diminished with continued survival of a patient. Leveraging logistic regression models and the

SUBMITTER: Qin C 

PROVIDER: S-EPMC12788376 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature