@inproceedings{holderness-etal-2018-analysis,
title = "Analysis of Risk Factor Domains in Psychosis Patient Health Records",
author = "Holderness, Eben and
Miller, Nicholas and
Bolton, Kirsten and
Cawkwell, Philip and
Meteer, Marie and
Pustejovsky, James and
Hua-Hall, Mei",
editor = "Lavelli, Alberto and
Minard, Anne-Lyse and
Rinaldi, Fabio",
booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5615",
doi = "10.18653/v1/W18-5615",
pages = "129--138",
abstract = "Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.",
}
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<abstract>Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.</abstract>
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%0 Conference Proceedings
%T Analysis of Risk Factor Domains in Psychosis Patient Health Records
%A Holderness, Eben
%A Miller, Nicholas
%A Bolton, Kirsten
%A Cawkwell, Philip
%A Meteer, Marie
%A Pustejovsky, James
%A Hua-Hall, Mei
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F holderness-etal-2018-analysis
%X Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.
%R 10.18653/v1/W18-5615
%U https://aclanthology.org/W18-5615
%U https://doi.org/10.18653/v1/W18-5615
%P 129-138
Markdown (Informal)
[Analysis of Risk Factor Domains in Psychosis Patient Health Records](https://aclanthology.org/W18-5615) (Holderness et al., Louhi 2018)
ACL
- Eben Holderness, Nicholas Miller, Kirsten Bolton, Philip Cawkwell, Marie Meteer, James Pustejovsky, and Mei Hua-Hall. 2018. Analysis of Risk Factor Domains in Psychosis Patient Health Records. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 129–138, Brussels, Belgium. Association for Computational Linguistics.