Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction (D19-62)
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Elena Alvarez-Mellado, Eben Holderness, Nicholas Miller, Fyonn Dhang, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
| Challenge: | Previously, readmission risk classifications rely on structured information, such as sociodemographic data, comorbidity codes and physiological variables. |
| Approach: | They propose to incorporate additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
| Outcome: | The proposed model incorporates topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
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