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

BC models substantially underpredicted diagnosis likelihood and inappropriately assigned lower probability scores to individuals with earlier censoring. Common filtering strategies did not adequately address this limitation. TTE approaches, particularly DTNN, effectively mitigated bias from the censoring distribution, resulting in superior discrimination and calibration performance and more accurate prediction of clinical prevalence. Machine learning practitioners should recognize the limitations of BC for long-horizon diagnosis prediction and adopt TTE approaches. The DTNN in particular is well-suited to mitigate the effects of right-censoring and maximize prediction performance in this setting.