A Concept for Graph-Based Temporal Similarity of Patient Data.

ABSTRACT

Computer-based decision support systems are often used for dedicated tasks such as the detection of sepsis. However, positive predictive values for sepsis detection are reported to achieve only around 46%. In this paper we describe a novel approach to use temporal data of electronic patient records based on similarity measures. We apply the concept of case-based reasoning, which is well-established in many fields of medical informatics. Temporal patient data are organized in a time-graph structure. For the quantification of similarity between cases, we exploit graph theory based approaches. For development and evaluation of our time-graph similarity frame we use the open MIMIC III dataset. In a later phase, we envision to transfer our concept from sepsis to other diseases.