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        <title>Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries
%A Ding, Xiyu
%A Hall, Mei-Hua
%A Miller, Timothy
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 3rd Clinical Natural Language Processing Workshop
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ding-etal-2020-incorporating
%X 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.
%R 10.18653/v1/2020.clinicalnlp-1.4
%U https://aclanthology.org/2020.clinicalnlp-1.4/
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.4
%P 35-40

Markdown (Informal)

[Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries](https://aclanthology.org/2020.clinicalnlp-1.4/) (Ding et al., ClinicalNLP 2020)

• Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries (Ding et al., ClinicalNLP 2020)

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.