Enhancing Antibiotic Stewardship using a Natural Language Approach for Better Feature Representation

TL;DR

This paper proposes transforming electronic health record data into text and using pretrained models to improve antibiotic susceptibility predictions, aiming to enhance antibiotic stewardship and combat resistance.

Contribution

It introduces a novel textual data representation of EHRs combined with foundation models to improve interpretability in antibiotic susceptibility prediction.

Findings

Text representation improves model interpretability

Foundation models enhance prediction accuracy

Supports better antibiotic stewardship decisions

Abstract

The rapid emergence of antibiotic-resistant bacteria is recognized as a global healthcare crisis, undermining the efficacy of life-saving antibiotics. This crisis is driven by the improper and overuse of antibiotics, which escalates bacterial resistance. In response, this study explores the use of clinical decision support systems, enhanced through the integration of electronic health records (EHRs), to improve antibiotic stewardship. However, EHR systems present numerous data-level challenges, complicating the effective synthesis and utilization of data. In this work, we transform EHR data into a serialized textual representation and employ pretrained foundation models to demonstrate how this enhanced feature representation can aid in antibiotic susceptibility predictions. Our results suggest that this text representation, combined with foundation models, provides a valuable tool to increase interpretability and support antibiotic stewardship efforts.