Mohktar Mas S, Basilakis Jim, Redmond Stephen J, Lovell Nigel H
Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2052, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6166-9. doi: 10.1109/IEMBS.2010.5627766.
The objectives of this paper are to present a guideline-based decision support system (GBDSS) design for supporting patient telehealth management of chronic disease and to test its performance in correctly making referral recommendations using routinely recorded measurement data from home telehealth recordings. The GBDSS has been developed to manage lung disease patients in a home telehealth environment. The system operates by checking the availability of home telehealth measurement data on a daily basis, interprets these data using a rule-based decision tree classification, and ultimately generates referral recommendations based on these measured data. The system has demonstrated discriminative power when applied in the analysis of retrospective telehealth data, as a surrogate for realtime referral generation. To this end a telehealth dataset comprising 16 chronic obstructive pulmonary disease (COPD) patients monitored over a 12 month period was used. It was shown that GBDSS referral recommendations could help reduce the number of cases that required a carer's urgent attention by 72.1%, with 81.9% accuracy, 80.8% specificity and 90.4% sensitivity.
本文的目的是提出一种基于指南的决策支持系统（GBDSS）设计，以支持慢性病患者的远程医疗管理，并使用来自家庭远程医疗记录的常规记录测量数据来测试其在正确做出转诊建议方面的性能。GBDSS已开发用于在家庭远程医疗环境中管理肺病患者。该系统通过每天检查家庭远程医疗测量数据的可用性来运行，使用基于规则的决策树分类来解释这些数据，并最终根据这些测量数据生成转诊建议。当应用于回顾性远程医疗数据分析时，该系统已证明具有判别能力，可作为实时转诊生成的替代方法。为此，使用了一个远程医疗数据集，该数据集包括16名在12个月期间接受监测的慢性阻塞性肺疾病（COPD）患者。结果表明，GBDSS转诊建议可以帮助将需要护理人员紧急关注的病例数量减少72.1%，准确率为81.9%，特异性为80.8%，敏感性为90.4%。
