Real-World Computable Phenotypes of Patient-Reported Disability in Multiple Sclerosis

2025 Informatics Summit On Demand

Presentation Time: 10:00 AM - 10:15 AM

Abstract Keywords: EHR-based Phenotyping, Machine Learning, Generative AI, and Predictive Modeling, Secondary Use of EHR Data, Real-World Evidence and Policy Making
Primary Track: Clinical Research Informatics
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

Common tools to measure multiple sclerosis (MS) disability are rarely available in the real-world clinical setting. Leveraging electronic health records (EHR) data and disability outcomes from two independent EHR-linked MS research registries, we aimed to develop test and validate computable phenotypes of patient-reported MS disability status. After multiple model iterations, random forest model containing ±6 months of codified EHR data alone reaches potentially clinically actionable accuracy and concordance index while still being the most pragmatic for clinical deployment. Our pragmatic computable phenotypes of patient-reported disability could improve MS patient monitoring at the point of care enable large-scale clinical investigations, and may have clinical applications beyond MS.
Speaker(s):
Wen Zhu, M.D.
University of Pittsburgh

Author(s):
Wen Zhu, M.D. - University of Pittsburgh; Chenyi Chen, M.S. - University of Pittsburgh; Tanuja Chitnis, M.D. - Brigham and Women’s Hospital; Elmor Pineda, Pharm.D. - Genentech; Nicole Bonine, Ph.D. - Genentech; Zongqi Xia, MD, PhD - University of Pittsburgh School of Medicine;

Harnessing Diverse Populations to Advance Multiple Sclerosis Research

2025 Informatics Summit On Demand

Presentation Time: 10:15 AM - 10:30 AM

Abstract Keywords: EHR-based Phenotyping, Informatics Research/Biomedical Informatics Research Methods, Clinical and Research Data Collection, Curation, Preservation, or Sharing, Cohort Discovery, Open Science for Biomedical Research and Translational Medicine, Outcomes Research, Clinical Epidemiology, Population Health, Secondary Use of EHR Data, Machine Learning, Generative AI, and Predictive Modeling
Primary Track: Clinical Research Informatics
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

To advance MS research we created a demographically diverse multiple sclerosis (MS) cohort from All of Us using an unsupervised approach (2,030 MS cases, 30% non-White). MS polygenic risk score based on existing MS genomic map predicts MS well for European ancestry but poorly for African ancestry. Known non-genetic MS risk factors (obesity, smoking, vitamin D deficiency) showed consistent association across racial/ethnic groups. This recourse helps increase knowledge of MS risk in diverse populations.
Speaker(s):
Chen Hu, M.S.
University of Pittsburgh

Author(s):
Zongqi Xia, MD, PhD - University of Pittsburgh School of Medicine; Tianxi Cai, ScD - Harvard University;

Development and Implementation of Electronic Phenotyping Algorithms for Precision Medicine: A Framework for EHR-Based Clinical Trial Recruitment

2025 Informatics Summit On Demand

Presentation Time: 10:30 AM - 10:45 AM

Abstract Keywords: Clinical Genomics/Omics and Interventions Based on Omics Data, Clinical Decision Support for Translational/Data Science Interventions, EHR-based Phenotyping, Measuring Outcomes
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Implementation Science and Deployment in Informatics: Enabling Clinical and Translational Research

Germline genetic testing is increasingly recommended for conditions with genetic etiologies that influence medical management. However, it's underutilized due to barriers at system, patient, and clinician levels. This study will use a hybrid cluster randomized trial to test nudges, informed by behavioral economics, aimed at increasing genetic testing uptake. Rapid cycle optimization will ensure effective implementation in diverse healthcare settings.
Speaker(s):
Anurag Verma
University of Pennsylvania

Author(s):

A Phenotype Algorithm for Classification of Single Ventricle Physiology using Electronic Health Records

2025 Informatics Summit On Demand

Presentation Time: 10:45 AM - 11:00 AM

Abstract Keywords: EHR-based Phenotyping, Informatics Research/Biomedical Informatics Research Methods, Medical Imaging, Secondary Use of EHR Data
Primary Track: Clinical Research Informatics
Programmatic Theme: Translational Bioinformatics Using Multi-Modal Patient Data and AI

We developed a phenotyping algorithm for identifying individuals with single ventricle physiology based on data from the electronic health record. Our algorithm was developed using features extracted from a cohort of 1,020 patients with ferumoxytol-enhanced MRI scans seen at our institution. When evaluated on a separate, broader cohort of 2,500 patients with clinically-adjudicated congenital heart disease, our algorithm demonstrated an accuracy of 99.2% and sensitivity of 97.5%, exceeding the performance of existing published methods.
Speaker(s):
Hang Xu, Ph.D.
UCLA

Author(s):
Kim-Lien Nguyen, MD - University of California Los Angeles; William Hsu, PhD - University of California, Los Angeles; Pierangelo Renella, MD - Children's Hospital of Orange County; Ramin Badiyan, MD - University of California Los Angeles; Ziad Hindosh, MD - UCLA Health; Francisco Elisarraras, MD - UCLA Health; Bing Zhu, PhD - University of California Los Angeles; Majid Husain, MD - UCLA health; Gary Satou, MD - UCLA Health; Paul Finn, MD - University of California Los Angeles;

A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions

2025 Informatics Summit On Demand

Presentation Time: 11:00 AM - 11:15 AM

Abstract Keywords: Fairness and Disparity Research in Health Informatics, Open Science for Biomedical Research and Translational Medicine, Informatics Research/Biomedical Informatics Research Methods
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

For evidence from observational studies to be reliable, researchers must ensure that the patient populations
of interest are accurately defined. However, disease definitions can be extremely difficult to standardize and
implement accurately across different datasets and study requirements. Furthermore, in this context, they
must also ensure that populations are represented fairly to accurately reflect populations’ various demographic
dynamics and to not overgeneralize across non-applicable populations. In this work, we present a generalized
tool to assess the fairness of disease definitions by evaluating their implementation across common fairness
metrics. Our approach calculates fairness metrics and provides a robust method to examine coarse and
strongly intersecting populations across many characteristics. We highlight workflows when working with
disease definitions, provide an example analysis using an OMOP CDM patient database, and discuss
potential directions for future improvement and research.
Speaker(s):
Jacob Zelko, B.S.
Northeastern University Roux Institute

Author(s):