  Machine learning (ML) models are increasingly pivotal in automating clinical
decisions. Yet, a glaring oversight in prior research has been the lack of
proper processing of Electronic Medical Record (EMR) data in the clinical
context for errors and outliers. Addressing this oversight, we introduce an
innovative projections-based method that seamlessly integrates clinical
expertise as domain constraints, generating important meta-data that can be
used in ML workflows. In particular, by using high-dimensional mixed-integer
programs that capture physiological and biological constraints on patient
vitals and lab values, we can harness the power of mathematical "projections"
for the EMR data to correct patient data. Consequently, we measure the distance
of corrected data from the constraints defining a healthy range of patient
data, resulting in a unique predictive metric we term as "trust-scores". These
scores provide insight into the patient's health status and significantly boost
the performance of ML classifiers in real-life clinical settings. We validate
the impact of our framework in the context of early detection of sepsis using
ML. We show an AUROC of 0.865 and a precision of 0.922, that surpasses
conventional ML models without such projections.
