Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis.

Abstract

Electronic health records (EHRs) are information systems designed to collect and manage clinical data in order to support various clinical activities. They have emerged as valuable sources of data for outcomes research, offering vast repositories of patient information for analysis. Definitions for pediatric sepsis diagnosis are ambiguous, resulting in delayed diagnosis and treatment, highlighting the need for precise and efficient patient categorizing techniques. Nevertheless, the use of EHRs in research poses challenges. Although EHRs were originally created to document patient encounters, the medical coding was designed to satisfy billing requirements. As a result, EHR data may lack granularity, potentially leading to misclassification and incomplete representation of patient conditions. We compared data-driven ICD code categories to chart review using probabilistic graphical models (PGMs) due to their ability to handle uncertainty and incorporate prior knowledge. Overall, this paper demonstrates the potential of using PGMs to address these challenges and improve the analysis of ICD codes for sepsis outcomes research.