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        <title>Risk Factors Extraction from Clinical Texts based on Linked Open Data</title>
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        <namePart type="given">Svetla</namePart>
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        <namePart type="family">Angelova</namePart>
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    <name type="personal">
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    <abstract>This paper presents experiments in risk factors analysis based on clinical texts enhanced with Linked Open Data (LOD). The idea is to determine whether a patient has risk factors for a specific disease analyzing only his/her outpatient records. A semantic graph of “meta-knowledge” about a disease of interest is constructed, with integrated multilingual terms (labels) of symptoms, risk factors etc. coming from Wikidata, PubMed, Wikipedia and MESH, and linked to clinical records of individual patients via ICD–10 codes. Then a predictive model is trained to foretell whether patients are at risk to develop the disease of interest. The testing was done using outpatient records from a nation-wide repository available for the period 2011-2016. The results show improvement of the overall performance of all tested algorithms (kNN, Naive Bayes, Tree, Logistic regression, ANN), when the clinical texts are enriched with LOD resources.</abstract>
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%0 Conference Proceedings
%T Risk Factors Extraction from Clinical Texts based on Linked Open Data
%A Boytcheva, Svetla
%A Angelova, Galia
%A Angelov, Zhivko
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F boytcheva-etal-2019-risk
%X This paper presents experiments in risk factors analysis based on clinical texts enhanced with Linked Open Data (LOD). The idea is to determine whether a patient has risk factors for a specific disease analyzing only his/her outpatient records. A semantic graph of “meta-knowledge” about a disease of interest is constructed, with integrated multilingual terms (labels) of symptoms, risk factors etc. coming from Wikidata, PubMed, Wikipedia and MESH, and linked to clinical records of individual patients via ICD–10 codes. Then a predictive model is trained to foretell whether patients are at risk to develop the disease of interest. The testing was done using outpatient records from a nation-wide repository available for the period 2011-2016. The results show improvement of the overall performance of all tested algorithms (kNN, Naive Bayes, Tree, Logistic regression, ANN), when the clinical texts are enriched with LOD resources.
%R 10.26615/978-954-452-056-4_019
%U https://aclanthology.org/R19-1019
%U https://doi.org/10.26615/978-954-452-056-4_019
%P 161-167

Markdown (Informal)

[Risk Factors Extraction from Clinical Texts based on Linked Open Data](https://aclanthology.org/R19-1019) (Boytcheva et al., RANLP 2019)

• Risk Factors Extraction from Clinical Texts based on Linked Open Data (Boytcheva et al., RANLP 2019)

ACL

• Svetla Boytcheva, Galia Angelova, and Zhivko Angelov. 2019. Risk Factors Extraction from Clinical Texts based on Linked Open Data. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 161–167, Varna, Bulgaria. INCOMA Ltd..