UCLA

Knowledge about the biology, etiology, staging, and treatment of a disease can be found in a growing and disparate set of sources, including observational clinical data, scientific literature, and clinical guidelines. However, effectively utilizing these sources to support clinical decision making remains a challenge. One of the challenges stems from the need to integrate knowledge from multiple sources that have heterogeneous representation, while locating and appraising evidence relevant to an individual patient can also be an issue. The objective of this dissertation is the formulation of an intermediate representation that logically consolidates and standardizes knowledge fragments across these sources, along with the definition of operators on this representation that generate, in a principled manner, the needed elements to facilitate answering clinical queries to support evidence-based medicine (EBM). The contributions of this work are: (1) a standardized representation, called Phenomenon-Centric Data Model Plus (PCDM+), which adopts the probabilistic entity-relationship model and captures and structures information about a disease drawn from scientific literature and patient records, emphasizing population-level observations and evidence; and (2) a set of operators that retrieve and infer information about individual patients from the PCDM+ to inform clinical queries. This work is demonstrated and evaluated in the domain of intracranial aneurysm.