@文章{信息:doi/10.2196/28861，作者=“Hettiarachchi, Chirath和Daskalaki, Elena和Desborough, Jane和Nolan, Christopher J和O'Neal, David和Suominen, Hanna”，标题=“将多种输入集成到人工胰腺系统:叙述文献综述”，期刊=“JMIR糖尿病”，年=“2022”，月=“2月”，日=“24”，卷=“7”，数字=“1”，页=“e28861”，关键词=“糖尿病，1型;人工胰腺;算法;多元分析;胰岛素输注系统;背景:1型糖尿病(T1D)是一种慢性自身免疫性疾病，其胰岛素分泌不足损害机体葡萄糖稳态。皮下持续输注胰岛素是一种常用的治疗方法。人工胰腺系统(APS)使用连续的葡萄糖水平监测和连续的皮下注射胰岛素在一个闭环模式，结合控制器(或控制算法)。然而，由于饮食、运动、压力、睡眠、疾病、葡萄糖感知和胰岛素作用延迟以及认知负担等复杂因素，APS的操作具有挑战性。为了克服这些挑战，研究人员正在研究通过集成额外输入来增强APS，创建多输入APS (MAPS)。 Objective: The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D. Methods: A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01{\%}) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed. Results: Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials. Conclusions: The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life. ", issn="2371-4379", doi="10.2196/28861", url="https://diabetes.www.mybigtv.com/2022/1/e28861", url="https://doi.org/10.2196/28861", url="http://www.ncbi.nlm.nih.gov/pubmed/35200143" }