Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)^1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl⁻¹) and hyperglycaemia (>180 mg dl⁻¹), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform³ to generate over 50,000 glucose observations to train a k-nearest neighbours⁴ decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41–45%)⁵. In silico^3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.

1型糖尿病（T1D）的特征在于胰腺β细胞功能障碍和胰岛素消耗。超过40％的T1D患者通过多次注射长效基础和短效推注胰岛素来管理血糖，即所谓的每日多次注射（MDI）^1,2。给药错误可导致危及生命的低血糖事件（<70 mg dl ^-1）和高血糖症（> 180 mg dl ^-1），增加视网膜病变，神经病和肾病的风险。正在利用机器学习（人工智能）方法将决策支持纳入许多医学专业。在这里，我们报告了一种算法，该算法使用MDI治疗向患有T1D的成年人提供每周的胰岛素剂量推荐。我们采用了独特的虚拟平台³生成50,000多个葡萄糖观察值，以训练k近邻⁴决策支持系统（KNN-DSS）来识别高血糖或低血糖的原因，并从12项潜在建议中确定必要的胰岛素调整。根据内分泌学家的审查，经实际人类数据验证后，KNN-DSS算法与董事会认证的内分泌学家达成了67.9％的总体协议，并提供了安全的建议。医师推荐的胰岛素泵治疗调整之间的比较表明，内分泌科医生之间的完全一致率为41.2％，这与以前的医师间一致意见的测量方法相符（41–45％）⁵。硅^3,6使用美国食品和药物管理局接受的用于评估人造胰腺技术的平台进行基准测试，表明使用KNN-DSS 12周后血糖结果有了实质性改善。我们的数据表明，KNN-DSS可以及早发现危险的胰岛素治疗方案，并可用于改善T1D患者的血糖结果并预防危及生命的并发症。