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Validation of Acute Pancreatitis Using Machine Learning and Multi-Site Adaptation for Anaphylaxis

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

A general methodological framework for developing improved health outcome of interest identification algorithms in the Sentinel Distributed Databases (SDD) using machine learning and natural language processing (NLP) techniques is being developed for the ongoing Sentinel project, “Validation of Anaphylaxis Using Machine Learning.”

This project will build on the anaphylaxis algorithm development work at one Data Partner and will have four aims:

  • Aim 1: Extend and evaluate the applicability of the ongoing anaphylaxis algorithm development work at one Data Partner by scaling the algorithm and case identification procedures for use at a second Sentinel Data Partner. 
  • Aim 2: Use and evaluate the ability of the general framework built for anaphylaxis to conduct chart review for acute pancreatitis.
  • Aim 3: Use and evaluate the ability of the general framework built for anaphylaxis to conduct “deep annotation” of validated acute pancreatitis cases.
  • Aim 4: Given the establishment of a “ground truth” of validated acute pancreatitis cases using expert medical chart review, use and evaluate the ability of the general framework built for anaphylaxis to conduct machine learning and utilize NLP techniques to develop risk prediction models. The aim is to improve the accuracy with which acute pancreatitis can be identified using structured and unstructured electronic data. 
Information
Time Period
April 2019 – March 2021
HOI Study Type
Novel Approaches to More Efficient Outcome Validation
Data Source(s)
Sentinel Distributed Database (SDD)
Related Links
Related Link(s)
Validation of Anaphylaxis Using Machine Learning
Extending Machine Learning Methods Development in Sentinel: Follow-up Analyses for Anaphylaxis Algorithm and Formalization of a General Phenotyping Framework (Phase 3)
Validation of Acute Pancreatitis Among Adults in an Integrated Healthcare System
Workgroup Leader(s)

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

Workgroup Member(s)

Adebola Ajao, PhD; Robert Ball, MD, MPH, ScM; Steven Bird, PharmD, PhD, MS; Sara Karami, PhD, MPH; Michael Nguyen, MD; Danijela Stojanovic PharmD, PhD; Mingfeng Zhang, MD, PhD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD

Yong Ma, PhD; Yueqin Zhao, PhD, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 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.