Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Kexin Huang, Abhishek Singh, Sitong Chen, Edward T. 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.
Topics & Concepts
Computer scienceScripting languageRepresentation (politics)Sequence (biology)Mechanical ventilationArtificial intelligenceMachine learningData miningProgramming languageMedicinePolitical sciencePsychiatryGeneticsLawPoliticsBiologyRespiratory Support and MechanismsSepsis Diagnosis and TreatmentMachine Learning in Healthcare