口服食物挑战（OFC）对于准确诊断患者的食物过敏至关重要。但是，患者不愿接受OFC，对于那些这样做的患者，在农村/社区医疗保健环境中，对过敏症患者的使用率有限。通过机器学习方法对OFC结果的预测可以促进在家中食品过敏原的删除，在OFC中改善患者和医师的舒适度，并通过最大程度地减少执行的OFC的数量来节省医疗资源。临床数据是从共同接受1,284个OFC的1,12例患者那里收集的，包括临床因素，包括血清特异性IgE，总IgE，皮肤刺测试（SPTS），症状，性别和年龄。使用这些临床特征，构建了机器学习模型，以预测花生，鸡蛋和牛奶挑战的结果。每种过敏原的最佳性能模型是使用凹入和凸内核（LUCCK）方法创建的，该方法在曲线（AUC）（AUC）下分别用于花生，鸡蛋和牛奶OFC预测为0.76、0.68和0.70， 。通过Shapley添加说明（SHAP）的模型解释表明，特定的IgE以及SPTS的Wheal和Flare值高度预测了OFC结果。该分析的结果表明，机器学习有可能预测OFC结果，并揭示了相关的临床因素进行进一步研究。

We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pretrained with IL. We show that the combination of IL and RL can match human results and that good performance strongly depends on combining the allocentric information with an egocentric representation of the environment.