医疗条件的共现：通过SNOMED代码的概率主题建模揭示模式
Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes
摘要：
与多种并存的健康状况相关的患者往往面临严重的并发症和不太有利的结果。合并症在患有肾脏疾病的个体中尤其普遍，这是一种日益普遍的疾病，影响了美国13%的普通人群。本研究旨在采用概率框架识别和表征患者中同时发生的医疗状况的模式。具体来说，我们以非传统的方式应用主题建模，以发现在超过13，000名诊断患有肾脏疾病的患者的EHR中分配和记录的SNOMEDCT代码之间的关联。与大多数以前的工作主题建模，我们应用的方法，而不是自然语言的代码。此外，我们定量评估的主题，评估其紧密性和独特性，并评估我们的结果的医学有效性。我们的实验表明，每个主题的特点是简洁的几个高度可能性和独特的疾病代码，表明主题是紧密的。此外，每对主题之间的主题间距离通常很高，说明了独特性。最后，在一个主题中组合在一起的大多数编码条件确实在医学文献中共同发生。值得注意的是，我们的研究结果揭示了一些迄今为止尚未在医学文献中报道为相关的条件之间的间接关联。
英文摘要：
Patients associated with multiple co-occurring health conditions often face aggravated complications and less favorable outcomes. Co-occurring conditions are especially prevalent among individuals suffering from kidney disease, an increasingly widespread condition affecting 13% of the general population in the US. This study aims to identify and characterize patterns of co-occurring medical conditions in patients employing a probabilistic framework. Specifically, we apply topic modeling in a non-traditional way to find associations across SNOMEDCT codes assigned and recorded in the EHRs of>13,000 patients diagnosed with kidney disease. Unlike most prior work on topic modeling, we apply the method to codes rather than to natural language. Moreover, we quantitatively evaluate the topics, assessing their tightness and distinctiveness, and also assess the medical validity of our results. Our experiments show that each topic is succinctly characterized by a few highly probable and unique disease codes, indicating that the topics are tight. Furthermore, inter-topic distance between each pair of topics is typically high, illustrating distinctiveness. Last, most coded conditions grouped together within a topic, are indeed reported to co-occur in the medical literature. Notably, our results uncover a few indirect associations among conditions that have hitherto not been reported as correlated in the medical literature.
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