论文标题
使用强化学习挑战自我监控I型糖尿病患者的普通批顾问
Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning
论文作者
论文摘要
自我监控的糖尿病患者必须在每顿饭前做出决定，应服用多少胰岛素。存在标准的推注顾问，但实际上从来没有被证明是最佳的。我们对使用强化学习技术进行了对使用T1DM模拟的数据进行质疑，T1DM是由FDA批准的模拟器由Kovatchev等人开发的。建模葡萄糖胰岛素相互作用。结果表明，最佳推注规则与标准推注顾问大不相同，如果遵循的话，实际上可以避免低血糖发作。
Patients with diabetes who are self-monitoring have to decide right before each meal how much insulin they should take. A standard bolus advisor exists, but has never actually been proven to be optimal in any sense. We challenged this rule applying Reinforcement Learning techniques on data simulated with T1DM, an FDA-approved simulator developed by Kovatchev et al. modeling the gluco-insulin interaction. Results show that the optimal bolus rule is fairly different from the standard bolus advisor, and if followed can actually avoid hypoglycemia episodes.
