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

27 Dec 2019EMNLP (ClinicalNLP) 2020 11arXiv:1912.11975archive 2025-07-28

Kexin Huang, Abhishek Singh, Sitong Chen, Edward T. Moseley, Chih-ying Deng, Naomi George, Charlotta Lindvall

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.

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kexinhuang12345/clinicalXLNet officialmentioned in papermentioned on GitHubpytorch report
kexinhuang12345/clinicalBERT mentioned on GitHubpytorch report
lindvalllab/clinicalXLNet mentioned on GitHubpytorch report

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AdamAttentionBPEDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSentencePieceSoftmaxXLNet

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