mlsanityData to 14 Oct 2026

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

arXiv 2405.20419 ↗First posted 30 May 2024cs.LG cs.AI cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

11 of 41 references.

References · 11

  1. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  2. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  3. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  4. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  5. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 373
  6. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 277
  7. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 105
  8. 2023MOTOR: A Time-To-Event Foundation Model For Structured Medical Recordscited by 78
  9. 2021CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction taskscited by 76
  10. 2021Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health recordscited by 54
  11. 2018Deep EHR: Chronic Disease Prediction Using Medical Notescited by 15

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Cited by year

Citing papers per year: 2024: 2; 2025: 6.

8 papers.

  1. 2025Clinical ModernBERT: An efficient and long context encoder for biomedical textcited by 26
  2. 2024Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learningcited by 17
  3. 2025A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMscited by 13
  4. 2024FEET: A Framework for Evaluating Embedding Techniquescited by 8
  5. 2025A Collection of Innovations in Medical AI for patient records in 2024cited by 7
  6. 2025Serialized EHR make for good text representationscited by 7
  7. 2025Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifierscited by 1
  8. 2025Temporal Entailment Pretraining for Clinical Language Models over EHR Datacited by 1