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.
J. Hugo and S. Ibing—These authors contributed equally to this work.
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Acknowledgements
This work is supported in part through the use of the research platform AI-Ready Mount Sinai (AIR.MS), and through the MSDW resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai. The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 826117 and by the Joachim-Herz foundation (to SI).
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Hugo, J. et al. (2023). Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records from a Large Integrated Health System. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_35
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