Abstract: The present invention is a modeling system and process for predicting individual outcomes and conditions from written database records of a population of individuals, using iterative variation of parameters. Individual subject documents are created by concatenation of unstructured text fields from the written database records of individuals, and these are processed using Natural Language Processing. An individual subject document corpus is built, and terms in the corpus are weighted and mapped to standard vocabularies. A term-by-document matrix is built and its dimensionality is reduced by Latent Semantic Indexing. Individual and term queries are combined and scored, producing a ranked list. The parameters of the model are iteratively optimized for an input list of individuals with corresponding condition, action, or outcome score values.

Abstract: A predictive modeling method implemented on a computer for predicting patient outcomes and conditions from medical database records of a population of patients, and an optimization process of iterative variation of parameters of the method to achieve a best precision fit. Individual patient documents are created by concatenation of unstructured text fields from the patient's medical record, and these are processed using Natural Language Processing. A patient document corpus is built, and terms in the corpus are weighted and mapped to standard vocabularies. A term-by-document matrix is built and its dimensionality is reduced by Latent Semantic Indexing. Patient and term queries are combined and scored, producing a ranked list. The parameters of the model are iteratively optimized for an input list of patients with corresponding health score values.

Abstract: This document presents a system and method to extract highly meaningful terms from unstructured fields in an EMR that distinguish two different patient populations. Using this system, healthcare providers can quickly identify terms that are highly associated with a specific set of patients, for instance, chronic heart failure (CHF) patients who are high utilizers of the emergency department (ED) compared to CHF patients with low ED utilization. The system enables healthcare providers to identify root causes of health outcomes and to discover potential targets for intervention and improving healthcare delivery.