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ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients' disease history

Bagheri, A; Sammani, A; van der Heijden, PGM; Asselbergs, FW; Oberski, DL; (2020) ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients' disease history. Journal of Intelligent Information Systems 10.1007/s10844-020-00605-w. (In press). Green open access

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Abstract

Given the rapid rate at which text data are being digitally gathered in the medical domain, there is growing need for automated tools that can analyze clinical notes and classify their sentences in electronic health records (EHRs). This study uses EHR texts to detect patients’ disease history from clinical sentences. However, in EHRs, sentences are less topic-focused and shorter than that in general domain, which leads to the sparsity of co-occurrence patterns and the lack of semantic features. To tackle this challenge, current approaches for clinical sentence classification are dependent on external information to improve classification performance. However, this is implausible owing to a lack of universal medical dictionaries. This study proposes the ETM (enrichment by topic modeling) algorithm, based on latent Dirichlet allocation, to smoothen the semantic representations of short sentences. The ETM enriches text representation by incorporating probability distributions generated by an unsupervised algorithm into it. It considers the length of the original texts to enhance representation by using an internal knowledge acquisition procedure. When it comes to clinical predictive modeling, interpretability improves the acceptance of the model. Thus, for clinical sentence classification, the ETM approach employs an initial TFiDF (term frequency inverse document frequency) representation, where we use the support vector machine and neural network algorithms for the classification task. We conducted three sets of experiments on a data set consisting of clinical cardiovascular notes from the Netherlands to test the sentence classification performance of the proposed method in comparison with prevalent approaches. The results show that the proposed ETM approach outperformed state-of-the-art baselines.

Type: Article
Title: ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients' disease history
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10844-020-00605-w
Publisher version: https://doi.org/10.1007/s10844-020-00605-w
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10105500
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