Supporting diagnostics and decision making in healthcare by modular methods of computational linguistics PROCEEDING
Lauri Lahti, Aalto University School of Science, Finland, Finland
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Washington, DC, United States Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA
We propose a new framework for development of modular computational methods to support processes of healthcare and health education in diverse settings. Motivated by an evaluation by The National Institute for Health and Welfare in Finland the proposed framework aims to address challenges of analyzing knowledge concerning healthcare services and patient records with computational linguistics. The framework aims to promote implementing personalized care in diagnostics, decision making, patient engagement and self-care. We describe some analysis methods of computational linguistics, natural language processing, statistics, algorithms and data mining. We have built a prototype program enabling representing and modifying health-related knowledge structures for purposes of prevention, diagnosis and care. For 25 most common diagnosis names we have identified dependencies of core symptom concepts in a conceptual co-occurrence network of 57 679 unique conceptual links about healthcare guidelines.
Lahti, L. (2016). Supporting diagnostics and decision making in healthcare by modular methods of computational linguistics. In Proceedings of E-Learn: World Conference on E-Learning (pp. 1513-1519). Washington, DC, United States: Association for the Advancement of Computing in Education (AACE). Retrieved September 24, 2018 from https://www.learntechlib.org/primary/p/174196/.
© 2016 Association for the Advancement of Computing in Education (AACE)
- Arocha, J., Wang, D., & Patel, V. (2005). Identifying reasoning strategies in medical decision making: A methodological guide. Journal of Biomedical Informatics. Volume 38, Issue 2, April 2005, Pages 154–171.
- Chmiel, A., Klimek, P., & Thurner, S. (2014). Spreading of diseases through comorbidity networks across life and gender. New Journal of Physics 16, 115013.
- Dasgupta, D., & Chawla, N. (2014). Disease and medication networks: an insight into disease-drug interactions. 2nd International Conference on Big Data and Analytics in Healthcare, Singapore 2014. Http://www3.nd.edu/~nchawla/papers/bdah_2.pdf Doumbouya, M., Kamsu-Foguem, B., Kenfack, H., & Foguem, C. (2015). Combining conceptual graphs and argumentation for aiding in the teleexpertise. Computers in Biology and Medicine, Elsevier, 2015, vol. 63, pp. 157-168.
Hoffman, A., Volk, R., Saarimaki, A., Stirling, C., Li, L., Härter, M., Kamath, G., & Llewellyn-Thomas, H. (2013). Delivering patient decision aids on the Internet: definitions, theories, current evidence, and emerging research areas. BMC Medical Informatics and Decision Making, 13(2):S13. DOI> 10.1186/1472-6947-13-S2-S13http://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-13-S2-S13#CR44Horsky, J., Schiff, G., Johnston, D., Mercincavage, L., Bell, D., & Middleton, B. (2012). Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions. Journal of Biomedical Informatics. Volume 45, Issue 6, December 2012, Pages 1202–1216. Http://dx.doi.org/10.1016/J.jbi.2012.09.002
- Lee, S. (2006). Visualization of clinical practice guidelines and patient care process. Doctoral dissertation. The George Washington University. Washington, DC, USA. Http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.473.5825&rep=rep1&type=pdf
- Ministry of Social Affairs and Health Finland (2016). Nationwide report of primary care service operation. Intermediary report 2. Published in Finnish. ISBN 978-952-00-3822-9. Http://urn.fi/URN:ISBN:978-952-00-3822-9 Riches, N., Panagioti, M., Alam, R., Cheraghi-Sohi, S., Campbell, S., Esmail, A., & Bower, P. (2016). The Effectiveness of Electronic Differential Diagnoses (DDX) Generators: A Systematic Review and Meta-Analysis. PLoS ONE 11(3): e0148991.
- Rowley, R. (2011). The 25 most common diagnoses. Robert Rowley, Chief Medical Officer, Practice Fusion EMR. Practice Fusion Blog, posted on 9 February 2011. Http://www.practicefusion.com/blog/25-most-common-diagnoses/ Semigran, H., Levine, D., Nundy, S., & Mehrotra, A. (2016). Comparison of Physician and Computer Diagnostic Accuracy. JAMA Internal Medicine. Published online October 10, 2016.
- Szolovits, P. (1995). Uncertainty and Decisions in Medical Informatics. Methods of Information in Medicine, 34:111–21.
- Terveyskirjasto (2016) Healthcare guidelines provided by The Finnish Medical Society Duodecim. Health library healthcare guidelines database. Http://www.terveyskirjasto.fi/terveyskirjasto/tk.koti The National Institute for Health and Welfare (2015). Preliminary report of nationwide data management of primary care services-Towards national information applicability. Published in Finnish. ISBN 978-952-302-498-4.
- World Health Organization (2016). From innovation to implementation– eHealth in the WHO European Region. WHO Regional Office for Europe. Http://www.euro.who.int/en/publications/abstracts/from-innovation-to-implementation-ehealth-inthe-who-european-region-2016
- Zhou, X., Menche, J., Barabasi, A., & Sharma, A. (2014). Human symptoms-disease network. Nature Communications, 5, 4212. Http://www.nature.com/articles/ncomms5212
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