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Found 10 papers, 3 papers with code
Updating the Minimum Information about CLinical Artificial Intelligence (MI-CLAIM) checklist for generative modeling research
In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the "Minimum information about clinical artificial intelligence modeling" (MI-CLAIM) checklist.
Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models
Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations.
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
In this study, we explored whether recent LLMs can reduce the need for large-scale data annotations.
CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference
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 clinical notes.
Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes
Our findings indicate that significant disparities exist among breast cancer patients receiving different types of therapies based on social determinants of health.
Cross-institution text mining to uncover clinical associations: a case study relating social factors and code status in intensive care medicine
Confirmation is needed with better text mining models, ideally on a larger manually labeled dataset.
Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors
We used word frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion.
Developing a general-purpose clinical language inference model from a large corpus of clinical notes
We sought to evaluate the impact of using a domain-specific vocabulary and a large clinical training corpus on the performance of these language models in clinical language inference.
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care
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 spacing between visits for each patient, with unequal numbers of data points collected across patients.
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
