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Found 13 papers, 3 papers with code
A Review of Challenges and Opportunities in Machine Learning for Health
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.
CheXclusion: Fairness gaps in deep chest X-ray classifiers
We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.
Ranked #1 on
Multi-Label Classification
on ChestX-ray14
Intimate Partner Violence and Injury Prediction From Radiology Reports
Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue.
Ethical Machine Learning in Health Care
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities.
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data.
Clustering Interval-Censored Time-Series for Disease Phenotyping
In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping.
Machine Learning for Health symposium 2022 -- Extended Abstract track
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA.
Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration
We aimed to build machine learning algorithms to identify pregnant patients and triage them by risk of complication to assist care management.
Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs
Cosine similarity values are computed between (1) all biological pathways starting at the considered drug and ending at the disease of interest and (2) all biological pathways starting at drugs currently prescribed against that disease and ending at the disease of interest.
NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs
Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications.
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.
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.
