Mullins Irene M, Siadaty Mir S, Lyman Jason, Scully Ken, Garrett Carleton T, Miller W Greg, Muller Rudy, Robson Barry, Apte Chid, Weiss Sholom, Rigoutsos Isidore, Platt Daniel, Cohen Simona, Knaus William A
Department of Public Health Sciences, University of Virginia Health System, Charlottesville, VA, USA.
Comput Biol Med. 2006 Dec;36(12):1351-77. doi: 10.1016/j.compbiomed.2005.08.003. Epub 2005 Dec 22.
Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations.
随着医疗保健系统整合患者信息以实现研究和利用目标，包含大量生物、临床和管理数据的临床存储库越来越容易获取。为了研究在这些数据库中搜索新颖见解的潜在价值，我们将一种新的数据挖掘方法HealthMiner应用于来自一个学术医疗系统的66.7万份住院和门诊数字记录的大型队列。HealthMiner使用三种无监督方法进行知识发现：临床挖掘（CliniMiner）、预测分析和模式发现。这项研究的初步结果表明，这些方法有可能通过识别潜在的新型临床疾病关联来扩展研究能力。
