Jimeng Sun

Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

2015 · PubMed

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 …

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

2016 · arXiv (Cornell University)

Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate …

LEAP

2017

Managing patients with complex multimorbidity has long been recognized as a difficult problem due to complex disease and medication dependencies and the potential risk of adverse drug interactions. Existing work either uses complicated rule-based protocols …

Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks

2022 · arXiv (Cornell University)

Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. In high-risk applications like healthcare, practitioners require $\textit{fully calibrated}$ probability predictions for decision-making. That is, conditioned on the prediction $\textit{vector}$, $\textit{every}$ class' probability …

MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement

2023 · arXiv (Cornell University)

Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design while overlooking the significance of data engineering. Medical tabular datasets frequently …

PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming

2023 · arXiv (Cornell University)

Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the scope to document-level relation extraction. Traditional relation …

Small Models are LLM Knowledge Triggers on Medical Tabular Prediction

2024 · arXiv (Cornell University)

Recent development in large language models (LLMs) has demonstrated impressive domain proficiency on unstructured textual or multi-modal tasks. However, despite with intrinsic world knowledge, their application on structured tabular data prediction still lags behind, primarily …

Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Coding

2025

Diagnosis-Related Group (DRG) codes are essential for hospital reimbursement and operations but require labor-intensive assignment. Large Language Models (LLMs) struggle with DRG coding due to the out-of-distribution (OOD) nature of the task: pretraining corpora rarely …

Matching clinicians with clinical trials using AI

2026 · Nature Health

Abstract Clinical trial-site selection is often inefficient, leading to low enrolment, poor participant diversity and costly delays. We developed DocTr, a cross-modal deep learning framework to optimize this process. DocTr uniquely integrates patient encounter data …