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.- Anthology ID:
- 2020.clinicalnlp-1.4
- Volume:
- Proceedings of the 3rd Clinical Natural Language Processing Workshop
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 35–40
- Language:
- URL:
- https://aclanthology.org/2020.clinicalnlp-1.4
- DOI:
- 10.18653/v1/2020.clinicalnlp-1.4
- Cite (ACL):
- Xiyu Ding, Mei-Hua Hall, and Timothy Miller. 2020. Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 35–40, Online. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries (Ding et al., ClinicalNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.clinicalnlp-1.4.pdf