Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data. In this work, we developed a new text representation Clinical XLNet for clinical notes which also 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.
@article{arxiv.1912.11975,
title = {Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation},
author = {Kexin Huang and Abhishek Singh and Sitong Chen and Edward T. Moseley and Chih-ying Deng and Naomi George and Charlotta Lindvall},
journal= {arXiv preprint arXiv:1912.11975},
year = {2019}
}