Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data

Abstract

Abstract In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant questions in healthcare. Furthermore, the availability of large-scale datasets opens the way for the integration of AI models into clinical practices. The ability to extract appropriate patient cohorts and large-scale datasets from hospital databases is vital in order to unlock the potential of real-world data collected in clinics and answer pivotal medical questions through retrospective studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach allowing access and interoperability. Even when the data are readily accessible, researchers need to systematically combine them to form study-specific cohorts with predefined baseline characteristics, tailored to answer specific research inquiries. This process is costly, repetitive, and error-prone, as it requires sifting through Electronic Medical Records, confirming ethical approval, verifying status of patient consent, checking the availability of imaging data, and filtering based on disease-specific image biomarkers. Our objective is to give the ability to craft study-specific patient cohorts to clinical researchers through an automated data preparation and processing pipeline. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopaedics.