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Validation of Anaphylaxis Using Machine Learning

Basic Details
Date Posted
Status
Complete
Health Outcome(s)
anaphylaxis
Description

The goal of this pilot project is to develop a draft framework to use machine learning and natural language processing (NLP) techniques to improve health outcome of interest (HOI) detection algorithms that may later be used in the larger Sentinel Distributed Database. This project has 4 aims: 

  • Aim 1: Conduct medical record chart validation of an adverse event outcome algorithm for anaphylaxis using machine-readable electronic medical records (i.e., not paper charts). 
  • Aim 2: Conduct “deep annotation” of charts to identify features consistent with the case definition that is being applied and features that the clinical experts use in their determination that aren’t captured by the case definition.  
  • Aim 3: Given the establishment of a “ground truth” of validated anaphylaxis cases using expert medical chart review, use machine learning and NLP techniques to develop risk prediction models that are designed to improve the accuracy with which anaphylaxis can be identified using structured and unstructured electronic data.
  • Aim 4: Leverage the work conducted in Aims 1-3 to draft a general methodological framework for developing improved HOI identification algorithms in the Sentinel Distributed Data Network using machine learning and NLP techniques.
Information
Time Period
July 2018 – December 2019
HOI Study Type
Novel Approaches to More Efficient Outcome Validation
Data Source(s)
Sentinel Distributed Database (SDD)
Related Links
Related Link(s)
Sentinel Methods Project: Automated Approaches to Anaphylaxis Case Classification Using Unstructured Data
Sentinel Methods Project: Validation of Acute Pancreatitis Using Machine Learning and Multi-site Adaptation for Anaphylaxis
Presentation: Electronic Health Records + Natural Language Processing + Machine Learning = Improved Sentinel Outcome Detection Algorithms
Publication: Identification and Validation of Anaphylaxis Using Electronic Health Data in a Population-Based Setting
Sentinel Methods Project: Extending Machine Learning Methods Development in Sentinel: Follow-up Analyses for Anaphylaxis Algorithm and Formalization of a General Phenotyping Framework (Phase 3)
Presentation: Improving Identification of Anaphylaxis
Presentation: Improving Methods of Identifying Anaphylaxis for Medical Product Safety Surveillance Using Natural Language Processing and Machine Learning
Publication: Improving Methods of Identifying Anaphylaxis for Medical Product Safety Surveillance Using Natural Language Processing and Machine Learning
Workgroup Leader(s)

Jennifer Nelson PhD; David Carrell PhD; Kaiser Permanente Washington Health Research Institute, Seattle, WA

Workgroup Member(s)

Robert Ball, MD, MPH, ScM; Steven Bird, PharmD, PhD, MS; Sara Karami, PhD, MPH; Danijela Stojanovic, PharmD, PhD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, MD

Yong Ma, PhD; Yueqin Zhao, PhD; Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, MD

David Cronkite, MS; Monica Fuji, MPH; Jing Zhou, PhD; Kaiser Permanente Washington Health Research Institute, Seattle, WA

James Floyd, MD, MS; University of Washington, Seattle, WA

Adi Bejan, PhD; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN

Kevin Haynes, PharmD, MSCE; HealthCore, Wilmington, DE

Brian Hazlehurst, PhD; Kaiser Permanente Center for Health Research, Kaiser Permanente Northwest, Portland, OR

Adee Kennedy, MS, MPH; Judith Maro, PhD; Mayura Shinde, PhD, MPH; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

Susan Gruber, PhD, MPH; Putnam Data Sciences, LLC.