Search Results for author: Irene Y. Chen

Found 9 papers, 1 papers with code

Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration

no code implementations26 May 2023 Hussein Mozannar, Yuria Utsumi, Irene Y. Chen, Stephanie S. Gervasi, Michele Ewing, Aaron Smith-McLallen, David Sontag

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

no code implementations28 Nov 2022 Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy

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

no code implementations13 Feb 2021 Irene Y. Chen, Rahul G. Krishnan, David Sontag

In this work, we focus on mitigating the interference of interval censoring in the task of clustering for disease phenotyping.

Clustering Time Series Analysis

Ethical Machine Learning in Health Care

no code implementations22 Sep 2020 Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities.

BIG-bench Machine Learning Ethics

CheXclusion: Fairness gaps in deep chest X-ray classifiers

1 code implementation14 Feb 2020 Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y. Chen, Marzyeh Ghassemi

We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.

Fairness Medical Diagnosis +2

Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph

no code implementations2 Oct 2019 Irene Y. Chen, Monica Agrawal, Steven Horng, David Sontag

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.

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