基于机器学习的胰岛素方案控制2型糖尿病患者
Insulin Regimen ML-based control for T2DM patients
AI总结：
本文通过马尔可夫决策过程建模2型糖尿病患者血糖水平，利用强化学习优化胰岛素治疗方案，以最大化长期奖励。
摘要：
我们通过随机过程建模2型糖尿病患者血糖水平（BGL），该过程主要由药物治疗方案（如胰岛素注射）决定，但并非仅此而已。BGL状态的变化还受到各种生理触发因素的影响，使该过程呈现出随机、统计上未知，但假设为准稳态的性质。为了表达处于理想健康BGL的激励，我们启发式地定义了一个奖励函数，该函数对 desirable BG 水平返回正值，对 undesirable BG 水平返回负值。状态空间包含足够多的状态以允许无记忆假设。这反过来允许构建马尔可夫决策过程（MDP），其目标是最大化长期总奖励。概率分布通过基于模型的强化学习（RL）找到，最优胰岛素治疗策略从MDP解中获取。
英文摘要：
\begin{abstract} We model individual T2DM patient blood glucose level (BGL) by stochastic process with discrete number of states mainly but not solely governed by medication regimen (e.g. insulin injections). BGL states change otherwise according to various physiological triggers which render a stochastic, statistically unknown, yet assumed to be quasi-stationary, nature of the process. In order to express incentive for being in desired healthy BGL we heuristically define a reward function which returns positive values for desirable BG levels and negative values for undesirable BG levels. The state space consists of sufficient number of states in order to allow for memoryless assumption. This, in turn, allows to formulate Markov Decision Process (MDP), with an objective to maximize the total reward, summarized over a long run. The probability law is found by model-based reinforcement learning (RL) and the optimal insulin treatment policy is retrieved from MDP solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
