WITHDRAWN: Developing a Generalizable Algorithm for Classifying COPD Using Electronic Health Record Data: Combining Expert Medical Curation and Surrogate-Assisted Feature Extraction

• Activity Number: 259 - SPEED: Missing Data and Causal Inference Methods, Part 2
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 3:05 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Su Chu and Jessica Lasky-Su and Michael Cho and Emily Wan and Scott Weiss and Elizabeth Karlson
• Companies: Harvard Medical School and Brigham and Women's Hospital and Harvard Medical School and Brigham and Women's Hospital and Harvard Medical School and Brigham and Women's Hospital and Harvard Medical School and Brigham and Women's Hospital and Harvard Medical School and Brigham and Women's Hospital and Harvard Medical School
• Keywords: electronic health record; biobanks; EMR; phenotyping; classification

Abstract

Large scale digitization of medical records has facilitated an unprecedented opportunity to uncover health patterns in disease risk, progression, and classification. However, the performance of algorithms developed within a single site (e.g. a particular hospital or biobank) may be reduced when applied to other medical record databases. In this work, we demonstrate principles to optimize generalizability of EMR-based classification algorithm development, and provide an example based in identifying chronic obstructive pulmonary disease, a highly heterogeneous lung disease, among patients in the Partners HealthCare Systems Biobank. Furthermore, using a combination of two feature space inputs: disease relevant concepts contributed by medical professionals and surrogate-assisted feature extraction for high throughput phenotyping, we port our algorithm to a secondary validation site and compare performance to demonstrate the generalizability of our approach.