9 citations · 12 across the 7 of their papers we have counts for
7 papers
The Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHR
Irsyad Adam, Zekai Chen, David Laprade +5
Large language models (LLMs) trained with next-word-prediction have achieved success as clinical foundation models. Representations from these language backbones yield strong linea…
Building the EHR Foundation Model via Next Event Prediction
Zekai Chen, Arda Pekis, Kevin Brown
Electronic Health Records (EHRs) contain rich temporal dynamics that conventional encoding approaches fail to adequately capture. While Large Language Models (LLMs) show promise fo…
GenVarFormer: Predicting gene expression from long-range mutations in cancer
David Laub, Ethan Armand, Arda Pekis +6
Distinguishing the rare "driver" mutations that fuel cancer progression from the vast background of "passenger" mutations in the non-coding genome is a fundamental challenge in can…
Patient-specific Biomolecular Instruction Tuning
Irsyad Adam, Zekai Chen, David Laub +3
Proteomics data is essential to pathogenic understanding of a disease phenotype. In cancer, analysis of molecular signatures enables precision medicine through the identification o…
Advancing High Resolution Vision-Language Models in Biomedicine
Zekai Chen, Arda Pekis, Kevin Brown
Multi-modal learning has significantly advanced generative AI, especially in vision-language modeling. Innovations like GPT-4V and open-source projects such as LLaVA have enabled r…
Language Models are Few-shot Learners for Prognostic Prediction
Zekai Chen, Mariann Micsinai Balan, Kevin Brown
Clinical prediction is an essential task in the healthcare industry. However, the recent success of transformers, on which large language models are built, has not been extended to…
Masked Image Modeling Advances 3D Medical Image Analysis
Zekai Chen, Devansh Agarwal, Kshitij Aggarwal +5
Recently, masked image modeling (MIM) has gained considerable attention due to its capacity to learn from vast amounts of unlabeled data and has been demonstrated to be effective o…