Evolving health consultancy by predictive caravan health sensing in developing countries

E. Kai, Ashir Ahmed, Sozo Inoue, Atsushi Taniguchi, N. Nakashima, Yasunobu Nohara, M. Kitsuregawa
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引用次数: 4

Abstract

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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发展中国家预测性大篷车健康感知发展中的健康咨询
本文介绍了一种基于机器学习的预测方法来改进健康咨询过程的预测方法。2012-2014年,我们在孟加拉国为2.2万多名篷车健康感应患者提供了健康咨询服务。在大篷车健康感知的健康咨询中,由于成本和工作量巨大,医生的任务成为整个过程的瓶颈,我们试图将其中一些任务委托给技能较差的卫生工作者。本文提出了一种从患者问诊、生命体征数据和主诉中预测医生建议的方法,并将此任务委托给卫生工作者,从而消除了瓶颈。在上述实验中,我们还对931名接受医生咨询的患者的建议预测的准确性进行了评估。查询和生命数据的预测准确率为76.24%,添加主诉数据的预测准确率为82.55%。
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