@inproceedings{feng-etal-2020-explainable,
title = "Explainable Clinical Decision Support from Text",
author = "Feng, Jinyue and
Shaib, Chantal and
Rudzicz, Frank",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.115",
doi = "10.18653/v1/2020.emnlp-main.115",
pages = "1478--1489",
abstract = "Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.",
}
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<abstract>Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.</abstract>
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%0 Conference Proceedings
%T Explainable Clinical Decision Support from Text
%A Feng, Jinyue
%A Shaib, Chantal
%A Rudzicz, Frank
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F feng-etal-2020-explainable
%X Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.
%R 10.18653/v1/2020.emnlp-main.115
%U https://aclanthology.org/2020.emnlp-main.115
%U https://doi.org/10.18653/v1/2020.emnlp-main.115
%P 1478-1489
Markdown (Informal)
[Explainable Clinical Decision Support from Text](https://aclanthology.org/2020.emnlp-main.115) (Feng et al., EMNLP 2020)
ACL
- Jinyue Feng, Chantal Shaib, and Frank Rudzicz. 2020. Explainable Clinical Decision Support from Text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1478–1489, Online. Association for Computational Linguistics.