Department of Advanced Information Technology, Kyushu University, Fukuoka 819-0395, Japan.
Institute of Decision Science for a Sustainable Society, Kyushu University, Fukuoka 819-0395, Japan.
Int J Environ Res Public Health. 2020 Mar 10;17(5):1806. doi: 10.3390/ijerph17051806.
The advancement of ICT and affordability of medical sensors enable healthcare data to be obtained remotely. Remote healthcare data is erroneous in nature. Detection of errors for remote healthcare data has not been significantly studied. This research aims to design and develop a software system to detect and reduce such healthcare data errors. Enormous research efforts produced error detection algorithms, however, the detection is done at the server side after a substantial amount of data is archived. Errors can be efficiently reduced if the suspicious data can be detected at the source. We took the approach to predict acceptable range of anthropometric data of each patient. We analyzed 40,391 records to monitor the growth patterns. We plotted the anthropometric items e.g., Height, Weight, BMI, Waist and Hip size for males and females. The plots show some patterns based on different age groups. This paper reports one parameter, height of males. We found three groups that can be classified with similar growth patterns: Age group 20-49, no significant change; Age group 50-64, slightly decremented pattern; and Age group 65-100, a drastic height loss. The acceptable range can change over time. The system estimates the updated trend from new health records.
信息技术的进步和医疗传感器的普及使得远程获取医疗保健数据成为可能。远程医疗保健数据本质上存在误差。远程医疗保健数据的错误检测尚未得到充分研究。本研究旨在设计和开发一种软件系统，以检测和减少此类医疗保健数据错误。尽管已经产生了大量的错误检测算法，但这些检测是在大量数据归档后在服务器端进行的。如果可以在源头上检测到可疑数据，则可以有效地减少错误。我们采取了预测每个患者的人体测量数据可接受范围的方法。我们分析了 40391 条记录来监测生长模式。我们绘制了人体测量项目，例如男性和女性的身高、体重、BMI、腰围和臀围。这些图表显示了基于不同年龄组的一些模式。本文报告了一个参数，即男性的身高。我们发现了三个可以根据相似的生长模式进行分类的组：年龄组 20-49，无明显变化；年龄组 50-64，略有减少的模式；年龄组 65-100，身高急剧下降。可接受范围可能会随时间而变化。该系统可以根据新的健康记录估计更新的趋势。
