Publications (9)
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction
Mingyu Derek Ma, Xiaoxuan Wang, Yijia Xiao +4
Clinical diagnosis prediction models, when provided with a patient's medical history, aim to detect potential diseases early, facilitating timely intervention and improving prognos…
Guided Discrete Diffusion for Electronic Health Record Generation
Jun Han, Zixiang Chen, Yongqian Li +4
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e.g., disease progression prediction, clinical trial design…
Extend and Explain: Interpreting Very Long Language Models
Joel Stremmel, Brian L. Hill, Jeffrey Hertzberg +3
While Transformer language models (LMs) are state-of-the-art for information extraction, long text introduces computational challenges requiring suboptimal preprocessing steps or a…
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh +4
Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understandin…
Surpassing GPT-4 Medical Coding with a Two-Stage Approach
Zhichao Yang, Sanjit Singh Batra, Joel Stremmel +1
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations. However, the GPT-4 LLM predic…
ReFACTor: Practical Low-Rank Matrix Estimation Under Column-Sparsity
Matan Gavish, Regev Schweiger, Elior Rahmani +1
Various problems in data analysis and statistical genetics call for recovery of a column-sparse, low-rank matrix from noisy observations. We propose ReFACTor, a simple variation of…