Validation of Anaphylaxis Using Machine Learning

Project Title Validation of Anaphylaxis Using Machine Learning
Date Posted
Thursday, October 11, 2018
Status
In progress
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.
Workgroup Leader(s)

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

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.
Health Outcome
anaphylaxis
Time Period
July 2018 – December 2019
Data Sources
Sentinel Distributed Database (SDD)
Related Links
Automated Approaches to Anaphylaxis Case Classification Using Unstructured Data
Validation of Acute Pancreatitis Using Machine Learning and Multi-site Adaptation for Anaphylaxis