Farzana Islam Adiba

Farzana Islam Adiba, Varsha Danduri, Fahmida Liza Piya et al.

The MIMIC-IV dataset is a large, publicly available electronic health record (EHR) resource widely used for clinical machine learning research. It comprises multiple modalities, including structured data, clinical notes, waveforms, and imaging data. Working with these disjointed modalities requires an extensive manual effort to preprocess and align them for downstream analysis. While several pipelines for MIMIC-IV data extraction are available, they target a small subset of modalities or do not fully support arbitrary downstream applications. In this work, we greatly expand our prior popular unimodal pipeline and present a comprehensive and customizable multimodal pipeline that can significantly reduce multimodal processing time and enhance the reproducibility of MIMIC-based studies. Our pipeline systematically integrates the listed modalities, enabling automated cohort selection, temporal alignment across modalities, and standardized multimodal output formats suitable for arbitrary static and time-series downstream applications. We release the code, a simple UI, and a Python package for selective integration (with embedding) at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.

Farzana Islam Adiba, Rahmatollah Beheshti

Large language models (LLMs) that are used in medical applications are known to show biased and unfair patterns. Prior to adopting these in clinical decision-making applications, it is crucial to identify these bias patterns to enable effective mitigation of their impact. In this study, we present a novel framework combining knowledge graphs (KGs) with auxiliary LLMs to systematically reveal complex bias patterns in medical LLMs. Specifically, the proposed approach integrates adversarial perturbation techniques to identify subtle bias patterns. The approach adopts a customized multi-hop characterization of KGs to enhance the systematic evaluation of arbitrary LLMs. Through a series of comprehensive experiments (on three datasets, six LLMs, and five bias types), we show that our proposed framework has noticeably greater ability and scalability to reveal complex biased patterns of LLMs compared to other baselines.