Abstract
Clinical Practice Guidelines (CPGs) are an essential resource for standardization and dissemination of medical knowledge. Adherence to these guidelines at the point of care or by the Clinical Decision Support System (CDSS) can greatly enhance the healthcare quality and reduce practice variations. However, CPG adherence is greatly impeded due to the variety of information held by these lengthy and difficult to parse text documents. In this research, we propose a mechanism for extracting meaningful information from CPGs, by transforming it into a structured format and training machine learning models including Naïve Bayes, Generalized Linear Model, Deep Learning, Decision Tree, Random Forest, and Ensemble Learner on that structured formatted data. Application of our proposed technique with the aforementioned models on Rhinosinusitis and Hypertension guidelines achieved an accuracy of 82.10%, 74.40%, 66.70%, 66.79%, 74.40%, and 83.94% respectively. Our proposed solution is not only able to reduce the processing time of CPGs but is equally beneficial to be used as a preprocessing step for other applications utilizing CPGs.
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Acknowledgement
This research was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00655). This work was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2017-0-01629) supervised by the Institute for Information & communications Technology Promotion (IITP) and NRF- 2016K1A3A7A03951968.
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Hussain, M., Lee, S. (2019). Information Extraction from Clinical Practice Guidelines: A Step Towards Guidelines Adherence. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_81
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DOI: https://doi.org/10.1007/978-3-030-19063-7_81
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