Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis.

ABSTRACT:

Background

A major challenge in using electronic health records (EHR) is the inconsistency of patient follow-up, resulting in right-censored outcomes. This becomes particularly problematic in long-horizon event predictions, such as autism and attention-deficit/hyperactivity disorder (ADHD) diagnoses, where a significant number of patients are lost to follow-up before the outcome can be observed. Consequently, fully supervised methods such as binary classification (BC), which are trained to predict observed diagnoses, are substantially affected by the probability of sufficient follow-up, leading to biased results.

Objective

This empirical analysis aims to characterize BC's inherent limitations for long-horizon diagnosis prediction from EHR; and quantify the benefits of a spec

SUBMITTER: Loh R 

PROVIDER: S-EPMC12223692 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature