Baig Mirza, Mirza Farhaan, GholamHosseini Hamid, Gutierrez Jairo, Ullah Ehsan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4456-4459. doi: 10.1109/EMBC.2018.8513343.
Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence and calories). The data was collected using an advanced wearable body vest. The real-time data was combined with manual recordings of blood glucose, height, weight, age and sex. The model analyzed the data alongside a clinical knowledge-base. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines and protocols. The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.
全球在长期和慢性护理疾病方面的支出正在增加，已达到需要立即进行干预和推进以减轻医疗成本负担的程度。这项研究专注于使用可穿戴技术早期检测糖尿病前期和2型糖尿病（T2DM）。基于自适应神经模糊推理开发了一种人工智能模型，通过个性化监测来检测糖尿病前期和T2DM。该模型的关键影响因素包括心率、心率变异性、呼吸频率、呼吸量和活动数据（步数、步频和卡路里）。数据通过先进的可穿戴式身体背心收集。实时数据与血糖、身高、体重、年龄和性别的手动记录相结合。该模型结合临床知识库对数据进行分析。通过现有干预措施、临床指南和方案使用模糊规则来建立基线值。所提出的模型使用卡帕分析进行测试和验证，总体一致性达到91%。
