Large observational clinical datasets are becoming increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete disease state develops through stereotypical routes, characterized by “points of no return" and “final states" (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow-up) observations.

中文翻译：

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大型临床数据集中的轨迹、分叉和伪时间：在心肌梗塞和糖尿病数据中的应用

大型观察性临床数据集越来越多地可用于挖掘各种疾病特征与所进行的治疗之间的关联。这些数据集可以被视为所有可能疾病状况的表征，其中具体的疾病状态通过刻板的路线发展，其特征是“不归路”和“最终状态”（例如致死或恢复状态）。直接从数据中提取这些信息仍然具有挑战性，尤其是在同步（短期随访）观察的情况下。