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首页> 外文会议>International Conference on Health Informatics >Method of Screening the Health of Persons with High Risk for Potential Lifestyle-related Diseases using LDA Toward a Better Screening Method for Persons with High Health Risks
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Method of Screening the Health of Persons with High Risk for Potential Lifestyle-related Diseases using LDA Toward a Better Screening Method for Persons with High Health Risks

机译：利用LDA筛选具有高风险的人的健康方法，利用LDA对健康风险高的人进行更好的筛选方法

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Recently, the number of patients with lifestyle-related diseases, such as diabetes mellitus, has increased dramatically. Lifestyle-related diseases are responsible for 60% of deaths in Japan. In order to screen persons at potentially high risk for these diseases, medical checkups for metabolic syndrome are used throughout Japan. Prediction and prevention of lifestyle-related diseases would yield a direct reduction in medical costs. However, many cases cannot be screened with a metabolic syndrome checkup. In this paper, we propose a new machine-learning-based screening method using medical checkup data and medical billings. By processing the medical data into a bag-of-words representation and classifying the health factors using latent Dirichlet allocation (LDA), the screening method achieves high accuracy. We evaluate the method by comparing the accuracy of predictions of the future incidence of the diseases. The results show that F-measure increases 0.17 compared with the conventional method. In addition, we confirmed that the proposed method classified persons with different health risk factors, such as a combination of metabolic disorders, hypertensive disorders, and mental disorders (stress).
机译：最近，糖尿病等生活方式相关疾病的患者的数量急剧增加。生活方式相关的疾病负责日本的60％死亡。为了筛选这些疾病的可能性高风险，在日本各地使用代谢综合征的体检。生活方式相关疾病的预测和预防将产生直接降低医疗费用。然而，许多情况不能用代谢综合征检查筛选。在本文中，我们提出了一种使用医疗检查数据和医学账单的新型机器学习的筛选方法。通过将医疗数据处理成一个单词袋式表示并使用潜在的Dirichlet分配（LDA）对健康因子进行分类，筛选方法达到高精度。通过比较未来疾病发生率的预测准确性来评估方法。结果表明，与传统方法相比，F测量值增加0.17。此外，我们证实，拟议的方法分类为不同的健康风险因素，例如代谢障碍，高血症和精神障碍（应力）的组合。

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