Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes

Emilien Arnaud; Mahmoud Elbattah; Pedro A. Moreno-Sanchez; Gilles Dequen; Daniel Aiham Ghazali

doi:10.1109/BigData59044.2023.10386753

Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes

Emilien Arnaud, Mahmoud Elbattah, Pedro A. Moreno-Sanchez, Gilles Dequen, Daniel Aiham Ghazali

Tutkimustuotos: Konferenssiartikkeli › Scientific › vertaisarvioitu

1 Sitaatiot (Scopus)

16 Lataukset (Pure)

Abstrakti

Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.

Alkuperäiskieli	Englanti
Otsikko	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Toimittajat	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Kustantaja	IEEE
Sivut	4843-4847
Sivumäärä	5
ISBN (elektroninen)	979-8-3503-2445-7
DOI - pysyväislinkit	https://doi.org/10.1109/BigData59044.2023.10386753
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Big Data - , Italia Kesto: 15 jouluk. 2023 → 18 jouluk. 2023

Conference

Conference	IEEE International Conference on Big Data
Lyhennettä	BigData
Maa/Alue	Italia
Ajanjakso	15/12/23 → 18/12/23

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Pääsy asiakirjaan

10.1109/BigData59044.2023.10386753

Explainable NLP Model for Predicting
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Accepted author manuscript, 182 KBLisenssi: Unspecified

https://urn.fi/URN:NBN:fi:tuni-202403062712Lisenssi: Unspecified

Siteeraa tätä

Arnaud, E., Elbattah, M., Moreno-Sanchez, P. A., Dequen, G., & Ghazali, D. A. (2023). Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. teoksessa J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Toimittajat), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (Sivut 4843-4847). IEEE. https://doi.org/10.1109/BigData59044.2023.10386753

Arnaud, Emilien ; Elbattah, Mahmoud ; Moreno-Sanchez, Pedro A. et al. / Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Toimittaja / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. IEEE, 2023. Sivut 4843-4847

@inproceedings{baa4261211664974a0d25bde4631115b,

title = "Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes",

abstract = "Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.",

keywords = "BERT, Emergency Department, Explainable Artificial Intelligence (XAI), Natural Language Processing, Transformers, Triage",

author = "Emilien Arnaud and Mahmoud Elbattah and Moreno-Sanchez, {Pedro A.} and Gilles Dequen and Ghazali, {Daniel Aiham}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; IEEE International Conference on Big Data, BigData ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

doi = "10.1109/BigData59044.2023.10386753",

language = "English",

pages = "4843--4847",

editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, {Jerry Chun-Wei} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "IEEE",

}

Arnaud, E, Elbattah, M, Moreno-Sanchez, PA, Dequen, G & Ghazali, DA 2023, Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. julkaisussa J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (toim), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. IEEE, Sivut 4843-4847, IEEE International Conference on Big Data, Italia, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386753

Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. / Arnaud, Emilien; Elbattah, Mahmoud; Moreno-Sanchez, Pedro A. et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. toim. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. IEEE, 2023. s. 4843-4847.

Tutkimustuotos: Konferenssiartikkeli › Scientific › vertaisarvioitu

TY - GEN

T1 - Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes

AU - Arnaud, Emilien

AU - Elbattah, Mahmoud

AU - Moreno-Sanchez, Pedro A.

AU - Dequen, Gilles

AU - Ghazali, Daniel Aiham

PY - 2023

Y1 - 2023

N2 - Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.

AB - Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize transformer models to leverage the extensive textual data captured in triage notes, while also delivering interpretable results by using the LIME approach. The results show that the proposed model provides satisfactory accuracy along with an interpretable understanding of the factors contributing to patient admission. In general, this work highlights the potential of NLP in improving patient care and decision-making in emergency medicine.

KW - BERT

KW - Emergency Department

KW - Explainable Artificial Intelligence (XAI)

KW - Natural Language Processing

KW - Transformers

KW - Triage

U2 - 10.1109/BigData59044.2023.10386753

DO - 10.1109/BigData59044.2023.10386753

M3 - Conference contribution

AN - SCOPUS:85184981614

SP - 4843

EP - 4847

BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

A2 - Palpanas, Themis

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Dou, Dejing

A2 - Slezak, Dominik

A2 - Wang, Wei

A2 - Gruca, Aleksandra

A2 - Lin, Jerry Chun-Wei

A2 - Agrawal, Rakesh

PB - IEEE

T2 - IEEE International Conference on Big Data

Y2 - 15 December 2023 through 18 December 2023

ER -

Arnaud E, Elbattah M, Moreno-Sanchez PA, Dequen G, Ghazali DA. Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. julkaisussa He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, toimittajat, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. IEEE. 2023. s. 4843-4847 doi: 10.1109/BigData59044.2023.10386753

Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes

Abstrakti

Conference

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

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