Evolving health consultancy by predictive caravan health sensing in developing countries

Eiko Kai, Sozo Inoue, Atsushi Taniguchi, Yasunobu Nohara, Ashir Ahmed, Naoki Nakashima, Masaru Kitsuregawa

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

In this paper, we introduce the predictive way to evolve the process of the health consultancy by predictive methods with machine learning. We have tried health consultancy for over 22,000 patients with caravan health sensing in Bangladesh during 2012-2014. In health consultancy with caravan health sensing, doctors' task becomes the bottleneck of the whole process because of the cost and the huge workload, and we try to delegate some of them to health workers who are less skilled. In this paper, we propose a method to predict the advices of doctors from the inquiry, vital data, and the chief complaints of the patients, and to delegate the task to health workers, resulting in eliminating the bottleneck. We also evaluate the accuracy of the prediction of advices from the 931 patients who have taken the doctors' consultancy out of the above experiment. We got the predict accuracy 76.24% with inquiry and vital data, and 82.55% with adding chief complaints data.

本文言語英語
ホスト出版物のタイトルUbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
出版社Association for Computing Machinery, Inc
ページ1225-1232
ページ数8
ISBN(電子版)9781450330473
DOI
出版ステータス出版済み - 1 1 2014
イベント2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 - Seattle, 米国
継続期間: 9 13 20149 17 2014

出版物シリーズ

名前UbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

その他

その他2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
国/地域米国
CitySeattle
Period9/13/149/17/14

All Science Journal Classification (ASJC) codes

  • ソフトウェア

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