Opening Access To Practice-based Evidence in Clinical Decision Support Systems with Natural Query Language

Paweł Kapłański, Alessandro Seganti, Krzysztof Cieśliński, Aleksandra Chrabrowa, Jerzy Koziolkiewicz, Marcin Bryk, Iwona Ługowska


Evidence-based medicine can be effective only if constantly tested against errors in medical practice. Clinical record database summarization supported by a machine allows allow to detect anomalies and therefore help detect the errors in early phases of care. Summarization system is a part of Clinical Decision Support Systems however it cannot be used directly by the stakeholder as long as s/he is not able to query the clinical record database. Natural Query Languages allow opening access to data for clinical practitioners, that usually do not have knowledge about articial query languages. Results: We have developed general purpose reporting system called Ask Data Anything (ADA) that we applied to a particular CDSS implementation. As a result, we obtained summarization system that opens the access for these of clinical researchers that were excluded from the meaningful summary of clinical records stored in a given clinical database. The most significant part of the component - NQL parser - is a hybrid of Controlled Natural Language (CNL) and pattern matching with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based (even erroneous) queries as well as complex CNL ones with the support of a predictive editor. By using ADA sophisticated summarizations of clinical data are produced as a result of NQL query execution. In this paper, we will present the main ideas underlying ADA component in the context of CDSS.


Clinical Decision Support System (CDSS), Natural Query Language (NQL)

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Data publikacji: 2017-12-22 09:38:06
Data złożenia artykułu: 2017-12-22 09:20:32


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