A Predictive Model for Patient Similarity: Classes Based on Secondary Data and Simple Measurements as Predictors

Predictive models optimized for average cases might work not perfect for cases deviating from average because they are based on a cohort of all patients. Models could be more personalized if they were built on a sub-cohort of patients similar to a current one and to train models on data collected from those similar patients. In this paper, we consider patient similarity as a classification task. We suppose that data such as diagnoses and treatment obtained by physicians (secondary data) are more relevant for similarity than tests and measurements (primary data). We defined several classes based on diagnoses and outcomes and apply a predictive model for classification. We used five commonly used and easy to obtain measurements as predictors for the model. All measurements were collected during the first 24 hours after admission. We have shown that classes of similar patients can be defined on the basis of a previous patient's secondary data and new patients can be classified into these classes.