Understanding and Reasoning About Early Signs of Sepsis - From Annotation Guideline to Ontology

Abstract

In the clinical domain, patient states such as sepsis due to bloodstream infection (BSI) result in observable symptoms and signs used to determine diagnosis and treatment, all of which often is documented in electronic health records. However, clinical text is brief and implicit, making it challenging to infer patient conditions by reasoning tasks and supervised machine learning. To study sepsis-related BSIs, we developed an ontology from an annotation guideline and annotated corpus that empirically captures BSIs from adverse event notes containing procedural deviations, guideline deviations, and unwanted incidents that can bring harm to patients. The resulting ontology represents (1) the physical patient state, clinical observations, and clinical documentation, and (2) background clinical knowledge for artificial intelligence, reasoning, and machine learning.