A4 Refereed article in a conference publication
A Machine Learning Approach Towards Early Detection of Frequent Health Care Users
Authors: Antti Airola, Tapio Pahikkala, Heljä Lundgrén-Laine, Anne Santalahti, Päivi Rautava, Sanna Salanterä, Tapio Salakoski
Editors: Suominen H
Conference name: International Louhi Workshop on Health Document Text Mining and Information Analysis
Publication year: 2013
Book title : Proceedings of the 4th International Louhi Workshop on Health Document Text Mining and Information Analysis (Louhi 2013)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/3535226
In primary health care, a small number of frequent users incur a large portion of the total health care expenditures. In this work, we study whether it is possible to recognize these frequent users early on, through the application of machine learning based text mining techniques on clinical notes. We implement our study on a data set of 147 Finnish primary health care users, using a regularized least-squares based ranking method. The method achieves a ranking accuracy of 0.68 when making predictions based on the recorded text and observed visitation frequency after 20 visitations by a patient, demonstrating that it is possible to make useful predictions about the future rate of visitations.
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