Supporting diagnostics and decision making in healthcare by modular methods of computational linguistics
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), San Diego, CA
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 March 26, 2019 from https://www.learntechlib.org/primary/p/174196/.
© 2016 Association for the Advancement of Computing in Education (AACE)
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