@inproceedings{huang-etal-2020-clinical,
title = "Clinical {XLN}et: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation",
author = "Huang, Kexin and
Singh, Abhishek and
Chen, Sitong and
Moseley, Edward and
Deng, Chih-Ying and
George, Naomi and
Lindvall, Charolotta",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.clinicalnlp-1.11",
doi = "10.18653/v1/2020.clinicalnlp-1.11",
pages = "94--100",
abstract = "Clinical notes contain rich information, which is relatively unexploited in predictive modeling compared to structured data. In this work, we developed a new clinical text representation Clinical XLNet that leverages the temporal information of the sequence of the notes. We evaluated our models on prolonged mechanical ventilation prediction problem and our experiments demonstrated that Clinical XLNet outperforms the best baselines consistently. The models and scripts are made publicly available.",
}
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<abstract>Clinical notes contain rich information, which is relatively unexploited in predictive modeling compared to structured data. In this work, we developed a new clinical text representation Clinical XLNet that leverages the temporal information of the sequence of the notes. We evaluated our models on prolonged mechanical ventilation prediction problem and our experiments demonstrated that Clinical XLNet outperforms the best baselines consistently. The models and scripts are made publicly available.</abstract>
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%0 Conference Proceedings
%T Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
%A Huang, Kexin
%A Singh, Abhishek
%A Chen, Sitong
%A Moseley, Edward
%A Deng, Chih-Ying
%A George, Naomi
%A Lindvall, Charolotta
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 3rd Clinical Natural Language Processing Workshop
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F huang-etal-2020-clinical
%X Clinical notes contain rich information, which is relatively unexploited in predictive modeling compared to structured data. In this work, we developed a new clinical text representation Clinical XLNet that leverages the temporal information of the sequence of the notes. We evaluated our models on prolonged mechanical ventilation prediction problem and our experiments demonstrated that Clinical XLNet outperforms the best baselines consistently. The models and scripts are made publicly available.
%R 10.18653/v1/2020.clinicalnlp-1.11
%U https://aclanthology.org/2020.clinicalnlp-1.11
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.11
%P 94-100
Markdown (Informal)
[Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation](https://aclanthology.org/2020.clinicalnlp-1.11) (Huang et al., ClinicalNLP 2020)
ACL