2.7k citations · 2.7k across the 14 of their papers we have counts for
14 papers
Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding
Shenghuan Sun, Alexander Schubert, Gregory M. Goldgof +4
Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions to assist in diagnostic and treatment tasks. However,…
The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)
Brenda Y. Miao, Irene Y. Chen, Christopher YK Williams +15
Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language an…
Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models
Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh +4
Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding th…
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
Madhumita Sushil, Travis Zack, Divneet Mandair +5
Although supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-co…
Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies
Yu-Ning Huang, Michael I. Love, Cynthia Flaire Ronkowski +13
Metadata, often termed "data about data," is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpre…
Large Language Models as Agents in the Clinic
Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz +3
Recent developments in large language models (LLMs) have unlocked new opportunities for healthcare, from information synthesis to clinical decision support. These new LLMs are not…
CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference
Madhumita Sushil, Vanessa E. Kennedy, Divneet Mandair +3
Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in c…
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback
Shenghuan Sun, Gregory M. Goldgof, Atul Butte +1
Generative models capable of capturing nuanced clinical features in medical images hold great promise for facilitating clinical data sharing, enhancing rare disease datasets, and e…
Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes
Shenghuan Sun, Travis Zack, Christopher Y. K. Williams +2
We aimed to investigate the impact of social circumstances on cancer therapy selection using natural language processing to derive insights from social worker documentation. We dev…
Cross-institution text mining to uncover clinical associations: a case study relating social factors and code status in intensive care medicine
Madhumita Sushil, Atul J. Butte, Ewoud Schuit +2
Objective: Text mining of clinical notes embedded in electronic medical records is increasingly used to extract patient characteristics otherwise not or only partly available, to a…
Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors
Shenghuan Sun, Travis Zack, Madhumita Sushil +1
Most research studying social determinants of health (SDoH) has focused on physician notes or structured elements of the electronic medical record (EMR). We hypothesize that clinic…
Developing a general-purpose clinical language inference model from a large corpus of clinical notes
Madhumita Sushil, Dana Ludwig, Atul J. Butte +1
Several biomedical language models have already been developed for clinical language inference. However, these models typically utilize general vocabularies and are trained on rela…
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care
Beau Norgeot, Dmytro Lituiev, Benjamin S. Glicksberg +1
Clinical data for ambulatory care, which accounts for 90% of the nations healthcare spending, is characterized by relatively small sample sizes of longitudinal data, unequal spacin…
Scalable and accurate deep learning for electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen +31
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical model…