Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records from a Large Integrated Health System

Julian Hugo, Susanne Ibing, Florian Borchert, Jan Philipp Sachs, Judy Cho, Ryan C. Ungaro, Erwin P. Böttinger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Early diagnosis and treatment of Crohn’s Disease (CD) is associated with decreased risk of surgery and complications. However, diagnostic delay is common in clinical practice. In order to better understand CD risk factors and disease indicators, we identified incident CD patients and controls within the Mount Sinai Data Warehouse (MSDW) and developed machine learning (ML) models for disease prediction. CD incident cases were defined based on CD diagnosis codes, medication prescriptions, healthcare utilization before first CD diagnosis, and clinical text, using structured Electronic Health Records (EHR) and clinical notes from MSDW. Cases were matched to controls based on sex, age and healthcare utilization. Thus, we identified 249 incident CD cases and 1,242 matched controls in MSDW. We excluded data from 180 days before first CD diagnosis for cohort characterization and predictive modeling. Clinical text was encoded by term frequency-inverse document frequency and structured EHR features were aggregated. We compared three ML models: Logistic Regression, Random Forest, and XGBoost. Gastrointestinal symptoms, for instance anal fistula and irritable bowel syndrome, are significantly overrepresented in cases at least 180 days before the first CD code (prevalence of 33% in cases compared to 12% in controls). XGBoost is the best performing model to predict CD with an AUROC of 0.72 based on structured EHR data only. Features with highest predictive importance from structured EHR include anemia lab values and race (white). The results suggest that ML algorithms could enable earlier diagnosis of CD and reduce the diagnostic delay.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Proceedings
EditorsJose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
PublisherSpringer Science and Business Media Deutschland GmbH
Pages293-302
Number of pages10
ISBN (Print)9783031343438
DOIs
StatePublished - 2023
Event21st International Conference on Artificial Intelligence in Medicine, AIME 2023 - Portoroz, Slovenia
Duration: 12 Jun 202315 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13897 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Artificial Intelligence in Medicine, AIME 2023
Country/TerritorySlovenia
CityPortoroz
Period12/06/2315/06/23

Keywords

  • Crohn disease
  • Diagnostic delay
  • Electronic health records

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