@inproceedings{damm-etal-2024-wispermed,
title = "{W}is{P}er{M}ed at {``}Discharge Me!{''}: Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on {MIMIC}-{IV}",
author = {Damm, Hendrik and
Pakull, Tabea Margareta Grace and
Ery{\i}lmaz, Bahad{\i}r and
Becker, Helmut and
Idrissi-Yaghir, Ahmad and
Sch{\"a}fer, Henning and
Schultenk{\"a}mper, Sergej and
Friedrich, Christoph M.},
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.9",
doi = "10.18653/v1/2024.bionlp-1.9",
pages = "105--121",
abstract = "This study aims to leverage state of the art language models to automate generating the {``}Brief Hospital Course{''} and {``}Discharge Instructions{''} sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians{'} administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities. This research was conducted within our participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various strategies were employed, including Few-Shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections. Utilizing an additional clinical domain-specific dataset demonstrated substantial potential to enhance clinical language processing. The DES method, which optimizes the selection of text outputs from multiple predictions, proved to be especially effective. It achieved the highest overall score of 0.332 in the competition, surpassing single-model outputs. This finding suggests that advanced deep learning methods in combination with DES can effectively automate parts of electronic health record documentation. These advancements could enhance patient care by freeing clinician time for patient interactions. The integration of text selection strategies represents a promising avenue for further research.",
}
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<abstract>This study aims to leverage state of the art language models to automate generating the “Brief Hospital Course” and “Discharge Instructions” sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians’ administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities. This research was conducted within our participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various strategies were employed, including Few-Shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections. Utilizing an additional clinical domain-specific dataset demonstrated substantial potential to enhance clinical language processing. The DES method, which optimizes the selection of text outputs from multiple predictions, proved to be especially effective. It achieved the highest overall score of 0.332 in the competition, surpassing single-model outputs. This finding suggests that advanced deep learning methods in combination with DES can effectively automate parts of electronic health record documentation. These advancements could enhance patient care by freeing clinician time for patient interactions. The integration of text selection strategies represents a promising avenue for further research.</abstract>
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%0 Conference Proceedings
%T WisPerMed at “Discharge Me!”: Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
%A Damm, Hendrik
%A Pakull, Tabea Margareta Grace
%A Eryılmaz, Bahadır
%A Becker, Helmut
%A Idrissi-Yaghir, Ahmad
%A Schäfer, Henning
%A Schultenkämper, Sergej
%A Friedrich, Christoph M.
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F damm-etal-2024-wispermed
%X This study aims to leverage state of the art language models to automate generating the “Brief Hospital Course” and “Discharge Instructions” sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians’ administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities. This research was conducted within our participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various strategies were employed, including Few-Shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections. Utilizing an additional clinical domain-specific dataset demonstrated substantial potential to enhance clinical language processing. The DES method, which optimizes the selection of text outputs from multiple predictions, proved to be especially effective. It achieved the highest overall score of 0.332 in the competition, surpassing single-model outputs. This finding suggests that advanced deep learning methods in combination with DES can effectively automate parts of electronic health record documentation. These advancements could enhance patient care by freeing clinician time for patient interactions. The integration of text selection strategies represents a promising avenue for further research.
%R 10.18653/v1/2024.bionlp-1.9
%U https://aclanthology.org/2024.bionlp-1.9
%U https://doi.org/10.18653/v1/2024.bionlp-1.9
%P 105-121
Markdown (Informal)
[WisPerMed at “Discharge Me!”: Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV](https://aclanthology.org/2024.bionlp-1.9) (Damm et al., BioNLP-WS 2024)
ACL
- Hendrik Damm, Tabea Margareta Grace Pakull, Bahadır Eryılmaz, Helmut Becker, Ahmad Idrissi-Yaghir, Henning Schäfer, Sergej Schultenkämper, and Christoph M. Friedrich. 2024. WisPerMed at “Discharge Me!”: Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 105–121, Bangkok, Thailand. Association for Computational Linguistics.