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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lohr, K.N., Field, M.J.: Clinical Practice Guidelines: Directions for a New Program. National Academies Press, Washington, D.C. (1990)
Wenzina, R., Kaiser, K.: Identifying condition-action sentences using a heuristic-based information extraction method. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds.) Process Support and Knowledge Representation in Health Care, pp. 26–38. Springer, Cham (2013)
Rello, J., Lorente, C., Bodí, M., Diaz, E., Ricart, M., Kollef, M.H.: Why do physicians not follow evidence-based guidelines for preventing ventilator-associated pneumonia?: a survey based on the opinions of an international panel of intensivists. Chest 122, 656–661 (2002)
Kilsdonk, E., Peute, L.W., Riezebos, R.J., Kremer, L.C., Jaspers, M.W.: From an expert-driven paper guideline to a user-centred decision support system: a usability comparison study. Artif. Intell. Med. 59, 5–13 (2013)
James, P.A., Oparil, S., Carter, B.L., Cushman, W.C., Dennison-Himmelfarb, C., Handler, J., Lackland, D.T., LeFevre, M.L., MacKenzie, T.D., Ogedegbe, O.: 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 311, 507–520 (2014)
Chow, A.W., Benninger, M.S., Brook, I., Brozek, J.L., Goldstein, E.J., Hicks, L.A., Pankey, G.A., Seleznick, M., Volturo, G., Wald, E.R.: IDSA clinical practice guideline for acute bacterial rhinosinusitis in children and adults. Clin. Infect. Dis. 54, e72–e112 (2012)
Jacobsen, P.B.: Clinical practice guidelines for the psychosocial care of cancer survivors. Cancer 115, 4419–4429 (2009)
Yetisgen-Yildiz, M., Gunn, M.L., Xia, F., Payne, T.H.: A text processing pipeline to extract recommendations from radiology reports. J. Biomed. Inform. 46, 354–362 (2013)
Khalifa, A., Meystre, S.: Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes. J. Biomed. Inform. 58, S128–S132 (2015)
Priyanta, S., Hartati, S., Harjoko, A., Wardoyo, R.: Comparison of sentence subjectivity classification methods in Indonesian News. Int. J. Comput. Sci. Inf. Secur. 14, 407 (2016)
Hematialam, H., Zadrozny, W.: Identifying condition-action statements in medical guidelines using domain-independent features. ArXiv Prepr. arXiv:1706.04206 (2017)
Hussain, M., Hussain, J., Sadiq, M., Hassan, A.U., Lee, S.: Recommendation statements identification in clinical practice guidelines using heuristic patterns. In: 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 152–156. IEEE (2018)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-19063-7_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19062-0
Online ISBN: 978-3-030-19063-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)