Abstract：
Sepsis is the third leading cause of death worldwide. Antibiotics are an important component in the treatment of sepsis. The use of antibiotics is currently facing the challenge of increasing antibiotic resistance (Evans et al., 2021). Sepsis medication prediction can be modeled as a Markov decision process, but existing methods fail to integrate with medical knowledge, making the decision process potentially deviate from medical common sense and leading to underperformance. (Wang et al., 2021). In this paper, we use Deep Q-Network (DQN) to construct a Sepsis Anti-infection DQN (SAI-DQN) model to address the challenge of determining the optimal combination and duration of antibiotics in sepsis treatment. By setting sepsis clinical knowledge as reward functions to guide DQN complying with medical guidelines, we formed personalized treatment recommendations for antibiotic combinations. The results showed that our model had a higher average value for decision-making than clinical decisions. For the test set of patients, our model predicts that 79.07% of patients will achieve a favorable prognosis with the recommended combination of antibiotics. By statistically analyzing decision trajectories and drug action selection, our model was able to provide reasonable medication recommendations that comply with clinical practices. Our model was able to improve patient outcomes by recommending appropriate antibiotic combinations in line with certain clinical knowledge.
摘要：
脓毒症是全球第三大死亡原因。抗生素是治疗脓毒症的重要组成部分。抗生素的使用目前面临着增加抗生素抗性的挑战(Evans等人。,2021)。脓毒症药物预测可以建模为马尔可夫决策过程，但是现有的方法无法与医学知识相结合，使决策过程可能偏离医学常识，导致业绩不佳。（Wang等人。,2021)。在本文中,我们使用深度Q-Network(DQN)构建脓毒症抗感染DQN(SAI-DQN)模型,以解决在脓毒症治疗中确定抗生素的最佳组合和持续时间这一难题.通过将败血症临床知识设置为奖励功能，以指导DQN遵守医学指南，我们为抗生素联合用药制定了个性化治疗建议.结果表明，我们的模型比临床决策具有更高的决策平均值。对于患者的测试集，我们的模型预测,79.07%的患者使用推荐的抗生素组合将获得良好的预后.通过统计分析决策轨迹和药物作用选择，我们的模型能够提供符合临床实践的合理用药建议.我们的模型能够通过根据某些临床知识推荐适当的抗生素组合来改善患者的预后。
