Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation

Kexin Huang, Abhishek Singh, Sitong Chen, Edward Moseley, Chih-Ying Deng, Naomi George, Charolotta Lindvall


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.
Anthology ID:
2020.clinicalnlp-1.11
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
94–100
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.11
DOI:
10.18653/v1/2020.clinicalnlp-1.11
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.11.pdf
Optional supplementary material:
 2020.clinicalnlp-1.11.OptionalSupplementaryMaterial.pdf