@inproceedings{rohr-etal-2024-revisiting,
title = "Revisiting Clinical Outcome Prediction for {MIMIC}-{IV}",
author = {R{\"o}hr, Tom and
Figueroa, Alexei and
Papaioannou, Jens-Michalis and
Fallon, Conor and
Bressem, Keno and
Nejdl, Wolfgang and
L{\"o}ser, Alexander},
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Bitterman, Danielle",
booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clinicalnlp-1.18",
doi = "10.18653/v1/2024.clinicalnlp-1.18",
pages = "208--217",
abstract = "Clinical Decision Support Systems assist medical professionals in providing optimal care for patients.A prominent data source used for creating tasks for such systems is the Medical Information Mart for Intensive Care (MIMIC).MIMIC contains electronic health records (EHR) gathered in a tertiary hospital in the United States.The majority of past work is based on the third version of MIMIC, although the fourth is the most recent version.This new version, not only introduces more data into MIMIC, but also increases the variety of patients.While MIMIC-III is limited to intensive care units, MIMIC-IV also offers EHRs from the emergency department.In this work, we investigate how to adapt previous work to update clinical outcome prediction for MIMIC-IV.We revisit several established tasks, including prediction of diagnoses, procedures, length-of-stay, and also introduce a novel task: patient routing prediction.Furthermore, we quantitatively and qualitatively evaluate all tasks on several bio-medical transformer encoder models.Finally, we provide narratives for future research directions in the clinical outcome prediction domain. We make our source code publicly available to reproduce our experiments, data, and tasks.",
}
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<abstract>Clinical Decision Support Systems assist medical professionals in providing optimal care for patients.A prominent data source used for creating tasks for such systems is the Medical Information Mart for Intensive Care (MIMIC).MIMIC contains electronic health records (EHR) gathered in a tertiary hospital in the United States.The majority of past work is based on the third version of MIMIC, although the fourth is the most recent version.This new version, not only introduces more data into MIMIC, but also increases the variety of patients.While MIMIC-III is limited to intensive care units, MIMIC-IV also offers EHRs from the emergency department.In this work, we investigate how to adapt previous work to update clinical outcome prediction for MIMIC-IV.We revisit several established tasks, including prediction of diagnoses, procedures, length-of-stay, and also introduce a novel task: patient routing prediction.Furthermore, we quantitatively and qualitatively evaluate all tasks on several bio-medical transformer encoder models.Finally, we provide narratives for future research directions in the clinical outcome prediction domain. We make our source code publicly available to reproduce our experiments, data, and tasks.</abstract>
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%0 Conference Proceedings
%T Revisiting Clinical Outcome Prediction for MIMIC-IV
%A Röhr, Tom
%A Figueroa, Alexei
%A Papaioannou, Jens-Michalis
%A Fallon, Conor
%A Bressem, Keno
%A Nejdl, Wolfgang
%A Löser, Alexander
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F rohr-etal-2024-revisiting
%X Clinical Decision Support Systems assist medical professionals in providing optimal care for patients.A prominent data source used for creating tasks for such systems is the Medical Information Mart for Intensive Care (MIMIC).MIMIC contains electronic health records (EHR) gathered in a tertiary hospital in the United States.The majority of past work is based on the third version of MIMIC, although the fourth is the most recent version.This new version, not only introduces more data into MIMIC, but also increases the variety of patients.While MIMIC-III is limited to intensive care units, MIMIC-IV also offers EHRs from the emergency department.In this work, we investigate how to adapt previous work to update clinical outcome prediction for MIMIC-IV.We revisit several established tasks, including prediction of diagnoses, procedures, length-of-stay, and also introduce a novel task: patient routing prediction.Furthermore, we quantitatively and qualitatively evaluate all tasks on several bio-medical transformer encoder models.Finally, we provide narratives for future research directions in the clinical outcome prediction domain. We make our source code publicly available to reproduce our experiments, data, and tasks.
%R 10.18653/v1/2024.clinicalnlp-1.18
%U https://aclanthology.org/2024.clinicalnlp-1.18
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.18
%P 208-217
Markdown (Informal)
[Revisiting Clinical Outcome Prediction for MIMIC-IV](https://aclanthology.org/2024.clinicalnlp-1.18) (Röhr et al., ClinicalNLP-WS 2024)
ACL
- Tom Röhr, Alexei Figueroa, Jens-Michalis Papaioannou, Conor Fallon, Keno Bressem, Wolfgang Nejdl, and Alexander Löser. 2024. Revisiting Clinical Outcome Prediction for MIMIC-IV. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 208–217, Mexico City, Mexico. Association for Computational Linguistics.