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A personalised and adaptive insulin dosing decision support system for type 1 diabetes

机译：1型糖尿病的个性化和适应性胰岛素给药决策支持系统

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People with type 1 diabetes (T1D) rely on exogenous insulin to maintain stable glucose levels. Despite the advent of diabetes technologies such as continuous glucose monitors and insulin infusion pumps, the majority of people with T1D do not manage to bring back glucose levels into a healthy target after meals. In addition to patient compliance, this is due to the complexity of the decision-making on how much insulin is required. Commercial insulin bolus calculators exist that help with the calculation of insulin for meals but these lack fine-tuning and adaptability.udThis thesis presents a novel insulin dosing decision support system for people with T1D that is able to provide individualised insulin dosing advice. The proposed research utilises Case-Based Reasoning (CBR), an artificial intelligence methodology, that is able to learn over time based on the behaviour of the patient and optimises the insulin therapy for various diabetes scenarios. The decision support system has been implemented into a user-friendly smartphone-based patient platform and communicates with a clinical platform for remote supervision.udIn-silico studies are presented demonstrating the overall performance of CBR as well as metrics used to adapt the insulin therapy. Safety and feasibility of the developed system have been assessed incrementally in clinical trials; initially during an eight-hour study in hospital settings followed by a six-week study in the home environment of the user. Human factors play an important role in the clinical adoption of technologies such as the one proposed. System usability and acceptability were evaluated during the second study phase based on feedback obtained from study participants.udResults from in-silico tests show the potential of the proposed research to safely automate the process of optimising the insulin therapy for people with T1D. In the six-week study, the system demonstrated safety in maintaining glycemic control with a trend suggesting improvement in postprandial glucose outcomes. Feedback from participants showed favourable outcomes when assessing device satisfaction and usability. A six-month large-scale randomised controlled study to evaluate the efficacy of the system is currently ongoing.
机译：患有1型糖尿病（T1D）的人依靠外源胰岛素来维持稳定的血糖水平。尽管出现了诸如连续血糖监测仪和胰岛素输注泵之类的糖尿病技术，但大多数患有T1D的人仍无法在饭后将血糖水平恢复到健康的目标。除了患者依从性外，这还归因于决策需要多少胰岛素的复杂性。现有商业胰岛素推注计算器可以帮助计算餐时胰岛素，但这些计算器缺乏微调和适应性。 ud本文提出了一种针对T1D患者的新型胰岛素剂量决策支持系统，该系统能够提供个性化的胰岛素剂量建议。拟议的研究利用了基于案例的推理（CBR），这是一种人工智能方法，能够随着时间的推移根据患者的行为进行学习，并针对各种糖尿病情况优化胰岛素治疗。决策支持系统已实施到基于用户友好的基于智能手机的患者平台中，并与临床平台进行通信以进行远程监管。 udIn-silico研究显示了CBR的整体性能以及用于调整胰岛素治疗的指标。已在临床试验中逐步评估了开发系统的安全性和可行性。最初是在医院环境中进行为时8小时的研究，然后是在用户的家庭环境中进行为期6周的研究。人为因素在诸如所建议的一项技术的临床采用中起着重要作用。在第二个研究阶段，根据研究参与者的反馈对系统的可用性和可接受性进行了评估。 ud计算机内测试的结果表明，所提出的研究具有潜力，可以安全地自动化优化T1D患者胰岛素治疗的过程。在为期六周的研究中，该系统证明了维持血糖控制的安全性，并具有表明餐后血糖结果改善的趋势。参与者的反馈在评估设备满意度和可用性时显示出良好的结果。目前正在进行为期六个月的大规模随机对照研究，以评估系统的功效。

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    Pesl Peter;

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