Disease specific ontology-guided rule engine and machine learning for enhanced critical care decision support

Abstract

A disease-specific ontology crafted by a consensus of expert clinicians may be used to semantically characterize/provide semantic meaning to dynamically changing patient electronic medical record (EMR) data in critical care settings. Hierarchical, directed node-edge-node graphs (concept maps or Vmaps) developed with an end-user friendly graphical user interface and ontology editor, can be used to represent structured clinical reasoning and serve as the first step in disease-specific ontology building. Disease domain Vmaps reflecting expert clinical reasoning associated with management of acute illnesses encountered in critical care settings (e.g. ICUs) that extend core clinical ontologies, developed and reviewed by experts, are in turn extended with existing medical ontologies and automatically translated to a domain ontology processing engine. Semantically-enhanced EMR data derived from the ontology processing engine is incorporated into both real-time 'track and trigger' rule engines and machine learning training algorithms using aggregated data. The resulting rule engines and machine-learnt models provide enhanced diagnostic and prognostic information respectively, to assist in clinical dual modes of reasoning (analytical rules and models based on experiential data) to assist in decisions associated with the specific disease in acute critical care settings.