@inproceedings{alvarez-mellado-etal-2019-assessing,
title = "Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction",
author = "Alvarez-Mellado, Elena and
Holderness, Eben and
Miller, Nicholas and
Dhang, Fyonn and
Cawkwell, Philip and
Bolton, Kirsten and
Pustejovsky, James and
Hall, Mei-Hua",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6211",
doi = "10.18653/v1/D19-6211",
pages = "81--86",
abstract = "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.",
}
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%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
Markdown (Informal)
[Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction](https://aclanthology.org/D19-6211) (Alvarez-Mellado et al., Louhi 2019)
ACL