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

arXiv: https://arxiv.org/abs/2405.20419 · HTML: https://mlsanity.com/paper/2405.20419/

## 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 (most cited first, 11 of 11)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks](https://mlsanity.com/paper/2111.08585/) (2021)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [Deep EHR: Chronic Disease Prediction Using Medical Notes](https://mlsanity.com/paper/1808.04928/) (2018)

## Built on by (most cited first, 8 of 8)

- [Clinical ModernBERT: An efficient and long context encoder for biomedical text](https://mlsanity.com/paper/2504.03964/) (2025)
- [Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning](https://mlsanity.com/paper/2406.13846/) (2024)
- [A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs](https://mlsanity.com/paper/2504.14657/) (2025)
- [FEET: A Framework for Evaluating Embedding Techniques](https://mlsanity.com/paper/2411.01322/) (2024)
- [A Collection of Innovations in Medical AI for patient records in 2024](https://mlsanity.com/paper/2503.05768/) (2025)
- [Serialized EHR make for good text representations](https://mlsanity.com/paper/2510.13843/) (2025)
- [Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers](https://mlsanity.com/paper/2503.03129/) (2025)
- [Temporal Entailment Pretraining for Clinical Language Models over EHR Data](https://mlsanity.com/paper/2504.18128/) (2025)

## 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.