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A Machine Learning Approach Towards Early Detection of Frequent Health Care Users

Antti Airola, Tapio Pahikkala, Heljä Lundgrén-Laine, Anne Santalahti, Päivi Rautava, Sanna Salanterä, Tapio Salakoski, A Machine Learning Approach Towards Early Detection of Frequent Health Care Users. In: Hanna Suominen (Ed.), Proceedings of the 4th International Louhi Workshop on Health Document Text Mining and Information Analysis, National ICT Australia, 2013.

Abstract:

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.

BibTeX entry:

@INPROCEEDINGS{inpAiPaLuSaRaSaSa13a,
  title = {A Machine Learning Approach Towards Early Detection of Frequent Health Care Users},
  booktitle = {Proceedings of the 4th International Louhi Workshop on Health Document Text Mining and Information Analysis},
  author = {Airola, Antti and Pahikkala, Tapio and Lundgrén-Laine, Heljä and Santalahti, Anne and Rautava, Päivi and Salanterä, Sanna and Salakoski, Tapio},
  editor = {Suominen, Hanna},
  publisher = {National ICT Australia},
  year = {2013},
  keywords = {clinical text mining, machine learning, regularized least-squares},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Turku BioNLP Group

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