Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patient outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses' expertise to achieve optimal care. Methods: This is a 6-month ancillary study among T2D patients (n = 20, age = 57 +- 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an online nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning-based Artificial Neural Network. The PDT was trained on participants self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient's glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance. Findings: The trained ONLC model achieved >=80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended.

翻译：背景：2型糖尿病是一种常见的慢性疾病，具有严重健康并发症和负面影响生活质量的显著风险。鉴于个体特征和生活方式对治疗方案及患者预后的影响，开发精准且个性化的管理策略至关重要。人工智能在结合来自多种数据源的模式与护士专业知识以实现最佳护理方面展现出巨大潜力。方法：这是一项为期6个月的辅助研究，对象为2型糖尿病患者（n=20，年龄=57±10）。参与者被随机分配至干预组（人工智能组，n=10）——在最后三个月接受每日人工智能生成的个体化反馈，或对照组（非人工智能组，n=10）——不接收每日反馈。研究开发了一个在线护士在环预测控制模型，该模型利用预测数字孪生。预测数字孪生采用基于迁移学习的人工神经网络构建。预测数字孪生使用参与者前三个月的自我监测数据（体重、饮食记录、体力活动、血糖）进行训练，在线控制算法应用粒子群优化来识别影响行为变化，以维持患者后三个月的血糖和体重水平。在线护士在环预测控制模型通过短信向干预组提供个体化反馈和建议。预测数字孪生每周重新训练以提升性能。发现：在线训练后，在线护士在环预测控制模型在所有患者中实现了≥80%的预测准确率。干预组参与者显示出每日步数改善及总热量和总碳水化合物摄入量按建议保持稳定或改善的趋势。