Search Results for author:
Found 9 papers, 1 papers with code
Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration
We focus on a high-risk pregnancy (HRP) program designed to reduce the likelihood of adverse prenatal, perinatal, and postnatal events and describe how we overcome three challenges of HRP programs as articulated by nurse care managers; (1) early detection of pregnancy, (2) accurate identification of impactable high-risk members, and (3) provision of explainable indicators to supplement predictions.
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.
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.
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data.
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.
Intimate Partner Violence and Injury Prediction From Radiology Reports
Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue.
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
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.
A Review of Challenges and Opportunities in Machine Learning for Health
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
