Abstract
Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.
Highlights
Modern artificial intelligence (AI)-based clinical decision support models owe their success in part to the very large number of predictors they use
Computational causal structure discovery (CSD) methods to discover causal relationships have demonstrated great success in many d omains[9,10,11] and their application to electronic health records (EHR) data could offer a solution for causal discovery from observational real world medical data
The temporal ordering of diseases may be reversed. For this reason, general purpose CSD algorithms applied to the EHR data occasionally report “causal” relationships that are in the opposite direction of the natural disease progression
Summary
Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Computational causal structure discovery (CSD) methods to discover causal relationships have demonstrated great success in many d omains[9,10,11] and their application to EHR data could offer a solution for causal discovery from observational real world medical data. To unlock their full potential, these general-purpose algorithms need to be adapted to address study design and data quality challenges specific to the EHR data. Leveraging the longitudinal nature of EHR data and incorporating time information as part of the causal discovery process can enhance the identification of edge orientation
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