TY - GEN
T1 - Evolving health consultancy by predictive caravan health sensing in developing countries
AU - Kai, Eiko
AU - Inoue, Sozo
AU - Taniguchi, Atsushi
AU - Nohara, Yasunobu
AU - Ahmed, Ashir
AU - Nakashima, Naoki
AU - Kitsuregawa, Masaru
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84908681068&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908681068&partnerID=8YFLogxK
U2 - 10.1145/2638728.2638816
DO - 10.1145/2638728.2638816
M3 - Conference contribution
AN - SCOPUS:84908681068
T3 - UbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 1225
EP - 1232
BT - UbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
T2 - 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
Y2 - 13 September 2014 through 17 September 2014
ER -