Chang Jonathan, Sarkar Indra Neil
Center for Biomedical Informatics, Brown University, Providence, RI.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:305-314. eCollection 2019.
Absent a priori knowledge, unsupervised techniques identify meaningful clusters that can form the basis for subsequent analyses. This study explored the problem of inferring comorbidity-based profiles of complex diseases through unsupervised clustering methodologies. This study first considered the K-Modes algorithm, followed by, the self organizing map (SOM) technique to extract co-morbidity based clusters from a healthcare discharge dataset. After validation of general cluster composition for diabetes mellitus, co-morbidity based clusters were identified for pregnancy. The SOM technique was found to infer distinct clusterings of pregnancy ranging from normal birth to preterm birth, and potentially interesting comorbidities that could be validated by published literature The promising results suggest that the SOM technique is a valuable unsupervised clustering method for discovering co-morbidity based clusters.
在缺乏先验知识的情况下，无监督技术可识别有意义的聚类，这些聚类可为后续分析奠定基础。本研究通过无监督聚类方法探讨了推断复杂疾病基于共病情况的特征这一问题。本研究首先考虑了K-Modes算法，随后采用自组织映射（SOM）技术从医疗出院数据集中提取基于共病情况的聚类。在验证了糖尿病的一般聚类构成后，确定了妊娠基于共病情况的聚类。结果发现，SOM技术能够推断出从正常分娩到早产的不同妊娠聚类，以及可能有趣的共病情况，这些情况可通过已发表的文献进行验证。这些有前景的结果表明，SOM技术是一种用于发现基于共病情况聚类的有价值的无监督聚类方法。
