Complicated multifactorial diseases deteriorate from one disease to other diseases. For example, existing studies consider Alzheimer’s disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without. It is important to understand the progress of disease that deteriorates to severe diseases. To this end, we develop a transitional phenotyping method based on both longitudinal and cross-sectional relationships between diseases and/or medications. For a cross-sectional approach, we utilized a skip-gram model to represent co-occurred disease or medication. For a longitudinal approach, we represented each patient as a transition probability between medical events and used supervised tensor factorization to decompose into groups of medical events that develop together. Then we harmonized both information to derive high-risk transitional patterns. We applied our method to disease progress from epilepsy to AD. An epilepsy-AD cohort of 600,000 patients were extracted from Cerner Health Facts data. Our experimental results suggested a causal relationship between epilepsy and later onset of AD, and also identified five epilepsy subgroups with distinct phenotypic patterns leading to AD. While such findings are preliminary, the proposed method combining representation learning with tensor factorization seems to be an effective approach for risk factor analysis.

复杂的多因素疾病从一种疾病恶化为其他疾病。例如，现有研究认为阿尔茨海默氏病（AD）是癫痫的合并症，但也认识到AD患者比非AD患者更容易发生癫痫。重要的是要了解恶化为严重疾病的疾病进展。为此，我们基于疾病和/或药物之间的纵向和横截面关系开发了一种过渡表型方法。对于横断面方法，我们使用了一个跳跃图模型来表示同时发生的疾病或药物。对于纵向方法，我们将每个患者表示为医疗事件之间的转移概率，并使用监督张量分解将其分解为一起发展的医疗事件组。然后，我们统一这两种信息以得出高风险的过渡模式。我们将我们的方法应用于从癫痫病到AD的疾病进展。从Cerner Health Facts数据中提取了600,000名患者的癫痫AD队列。我们的实验结果表明癫痫与AD的发病之间存在因果关系，还确定了五个癫痫亚组，它们具有导致AD的不同表型。尽管这些发现是初步的，但将表示学习与张量因子分解相结合的拟议方法似乎是进行风险因子分析的有效方法。我们的实验结果表明癫痫与AD的发病之间存在因果关系，还确定了五个癫痫亚组，它们具有导致AD的不同表型。尽管这些发现是初步的，但将表示学习与张量因子分解相结合的拟议方法似乎是进行风险因子分析的有效方法。我们的实验结果表明癫痫与AD的发病之间存在因果关系，还确定了五个癫痫亚组，它们具有导致AD的不同表型。尽管这些发现是初步的，但将表示学习与张量因子分解相结合的拟议方法似乎是进行风险因子分析的有效方法。