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Article Abstract
Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs. There are a number of risk factors that have been hypothesized to be important for understanding re-admission risk, including such factors as problems with substance abuse, ability to maintain work, relations with family. In this work, we develop RoBERTa-based models to predict the sentiment of sentences describing readmission risk factors in discharge summaries of patients with psychosis. We improve substantially on previous results by a scheme that shares information across risk factors while also allowing the model to learn risk factor-specific information.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729285 | PMC |
| http://dx.doi.org/10.18653/v1/2020.clinicalnlp-1.4 | DOI Listing |
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