Evolving Health Consultancy by Predictive Caravan Health Sensing in Developing Countries

Eiko Kai, Ashir Ahmed, Sozo Inoue, Naoki Nakashima, Masaru Kitsuregawa,
ACM UbiComp International Workshop on Smart Health Systems and Applications (SmartHealthSys)
(Not Available)
(Not Available)
Seattle, USA
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.

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