Overview

Clinical risk prediction estimates the likelihood and timing of outcomes such as deterioration, complications, readmission, or disease onset to support screening and care decisions. Methods range from logistic and Cox regression and clinical scores to gradient-boosted trees, survival models, temporal neural networks, and multimodal systems combining EHR data, notes, images, and wearable signals. Current work also explores calibration, uncertainty estimates, explainability, and privacy-preserving learning across hospitals. A central challenge is ensuring that strong results reflect genuine future risk and remain reliable across populations and settings, rather than being inflated by data leakage or weakened by distribution shift.

Latest papers

CardsList

218 papers21 papers in the last four weeks, down 5% on the four weeks before. 0.1% of all new papers.

  1. Extending ROC Analysis to Uncertainty-Aware Risk Prediction with an Interval-Based AUC (iAUC)

    Feb 4, 2026Yuqi Li, Matthew M. EngelhardUncertainty QuantificationClinical Risk Prediction

  2. Improving Forecasts of Suicide Attempts for Patients with Little Data

    Nov 22, 2025Genesis Hang, Annie Chen, Hope Neveux +2Latent Variable ModelsGaussian Processes

  3. GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification

    Aug 3, 2025Cheng Huang, Zeyu Han, Weizheng Xie +4Multimodal FusionClinical Risk Prediction

  4. Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure

    Mar 18, 2024Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed +4HealthcareOncology

  5. Subtyping patients with chronic disease using longitudinal BMI patterns

    Nov 9, 2021Md Mozaharul Mottalib, Jessica C Jones-Smith, Bethany Sheridan +1Clinical Risk Prediction