%0 Conference Proceedings %T Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction %A Alvarez-Mellado, Elena %A Holderness, Eben %A Miller, Nicholas %A Dhang, Fyonn %A Cawkwell, Philip %A Bolton, Kirsten %A Pustejovsky, James %A Hall, Mei-Hua %Y Holderness, Eben %Y Jimeno Yepes, Antonio %Y Lavelli, Alberto %Y Minard, Anne-Lyse %Y Pustejovsky, James %Y Rinaldi, Fabio %S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019) %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong %F alvarez-mellado-etal-2019-assessing %X Predicting which patients are more likely to be readmitted to a hospital within 30 days after discharge is a valuable piece of information in clinical decision-making. Building a successful readmission risk classifier based on the content of Electronic Health Records (EHRs) has proved, however, to be a challenging task. Previously explored features include mainly structured information, such as sociodemographic data, comorbidity codes and physiological variables. In this paper we assess incorporating additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. %R 10.18653/v1/D19-6211 %U https://aclanthology.org/D19-6211 %U https://doi.org/10.18653/v1/D19-6211 %P 81-86