62 citations · 137 across the 16 of their papers we have counts for
15 papers · 1 filter
A systematic review of trial-matching pipelines using large language models
Braxton A. Morrison, Madhumita Sushil, Jacob S. Young
Matching patients to clinical trial options is critical for identifying novel treatments, especially in oncology. However, manual matching is labor-intensive and error-prone, leadi…
Barriers in Integrating Medical Visual Question Answering into Radiology Workflows: A Scoping Review and Clinicians' Insights
Deepali Mishra, Chaklam Silpasuwanchai, Ashutosh Modi +2
Medical Visual Question Answering (MedVQA) is a promising tool to assist radiologists by automating medical image interpretation through question answering. Despite advances in mod…
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Felix J. Dorfner, Amin Dada, Felix Busch +8
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this appro…
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…
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…
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…
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…
Distilling neural networks into skipgram-level decision lists
Madhumita Sushil, Simon Šuster, Walter Daelemans
Several previous studies on explanation for recurrent neural networks focus on approaches that find the most important input segments for a network as its explanations. In that cas…
Why can't memory networks read effectively?
Simon Šuster, Madhumita Sushil, Walter Daelemans
Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering. While recent work revealed that memory networks can'…
Rule induction for global explanation of trained models
Madhumita Sushil, Simon Šuster, Walter Daelemans
Understanding the behavior of a trained network and finding explanations for its outputs is important for improving the network's performance and generalization ability, and for en…
Patient representation learning and interpretable evaluation using clinical notes
Madhumita Sushil, Simon Šuster, Kim Luyckx +1
We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient represen…
Unsupervised patient representations from clinical notes with interpretable classification decisions
Madhumita Sushil, Simon Šuster, Kim Luyckx +1
We have two main contributions in this work: 1. We explore the usage of a stacked denoising autoencoder, and a paragraph vector model to learn task-independent dense patient repres…