Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training 2 agents with different focuses: the primary agent studies the most recent visit of the patient to learn the current health status, and then the second-opinion agent considers the entire patient history to obtain a more global view.

中文翻译：

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Agent博士：通过模仿第二意见建立的临床预测模型。

疾病表型及其结果的预测是一项艰巨的任务。在实践中，患者通常会从多位临床专家那里寻求有关复杂疾病诊断的第二意见。我们的目标是通过训练2位重点不同的药物来模仿寻求第二意见的做法：主要药物研究患者的最近就诊以了解当前的健康状况，然后第二观点药物考虑整个患者的病史以获得更全面的视野。