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

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arxiv 2405.20419 v1 pith:YZ7R33HD submitted 2024-05-30 cs.LG cs.AIcs.CL

Simon A. Lee , Trevor Brokowski , Jeffrey N. Chiang This is my paper · ORCID
classification cs.LGcs.AIcs.CL
keywords antibioticrepresentationstewardshipantibioticscrisisdataenhancedfeature
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Temporal Entailment Pretraining for Clinical Language Models over EHR Data

    cs.CL 2025-04 conditional novelty 6.0 of 10

    A temporal entailment pretraining objective, which classifies later EHR segments as entailed, contradictory, or neutral relative to earlier segments, is claimed to improve clinical language models on temporal QA, earl...

  2. A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

    cs.CL 2025-04 conditional novelty 4.0 of 10

    Commercial LLMs generate usable synthetic ICU records only for small feature sets, with fidelity and downstream prediction quality degrading sharply as dimensionality grows.

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