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Machine learning (ML) models are increasingly pivotal in automating clinicaldecisions. Yet, a glaring oversight in prior research has been the lack ofproper processing of Electronic Medical Record (EMR) data in the clinicalcontext for errors and outliers. Addressing this oversight, we introduce aninnovative projections-based method that seamlessly integrates clinicalexpertise as domain constraints, generating important meta-data that can beused in ML workflows. In particular, by using high-dimensional mixed-integerprograms that capture physiological and biological constraints on patientvitals and lab values, we can harness the power of mathematical "projections"for the EMR data to correct patient data. Consequently, we measure the distanceof corrected data from the constraints defining a healthy range of patientdata, resulting in a unique predictive metric we term as "trust-scores". Thesescores provide insight into the patient's health status and significantly boostthe performance of ML classifiers in real-life clinical settings. We validatethe impact of our framework in the context of early detection of sepsis usingML. We show an AUROC of 0.865 and a precision of 0.922, that surpassesconventional ML models without such projections.

Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients