@inproceedings{van-aken-etal-2021-clinical,
title = "Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration",
author = "van Aken, Betty and
Papaioannou, Jens-Michalis and
Mayrdorfer, Manuel and
Budde, Klemens and
Gers, Felix and
Loeser, Alexander",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.75",
doi = "10.18653/v1/2021.eacl-main.75",
pages = "881--893",
abstract = "Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel *admission to discharge* task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose *clinical outcome pre-training* to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.",
}
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<abstract>Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel *admission to discharge* task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose *clinical outcome pre-training* to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.</abstract>
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%0 Conference Proceedings
%T Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
%A van Aken, Betty
%A Papaioannou, Jens-Michalis
%A Mayrdorfer, Manuel
%A Budde, Klemens
%A Gers, Felix
%A Loeser, Alexander
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F van-aken-etal-2021-clinical
%X Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel *admission to discharge* task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propose *clinical outcome pre-training* to integrate knowledge about patient outcomes from multiple public sources. We further present a simple method to incorporate ICD code hierarchy into the models. We show that our approach improves performance on the outcome tasks against several baselines. A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.
%R 10.18653/v1/2021.eacl-main.75
%U https://aclanthology.org/2021.eacl-main.75
%U https://doi.org/10.18653/v1/2021.eacl-main.75
%P 881-893
Markdown (Informal)
[Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration](https://aclanthology.org/2021.eacl-main.75) (van Aken et al., EACL 2021)
ACL