Electronic medical records: fast track to big data in bipolar disorder.
Electronic medical records: fast track to big data in bipolar disorder.
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电子病历：双相情感障碍大数据的快速通道。
DOI：
10.1176/appi.ajp.2015.15010043
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Potash,JamesB
中科院分区：
文献类型：
--
作者：
Potash,JamesB
A teacher of mine was fond of saying that “a man on a fast train can diagnose florid mania, as he speeds by and looks out the window at a patient”(Melvin G. McInnis, MD, personal communication). Despite the bit of hyperbole, there is truth to the idea that classic mania, and thus bipolar I disorder, often can be straightforward to recognize when it is directly encountered, or even when inquired about after the fact (1). Other forms of bipolar disorder, such as bipolar II, are more subtle, though careful examination can yield high-reliability diagnoses here as well (2).As reported in this issue of the Journal, Castro et al.(3) asked whether a man or woman on a fast computer could diagnose bipolar disorder. They took advantage of the power of the electronic health record (EHR) to identify more than 50,000 potential bipolar disorder cases. Manual review of a subset of these showed that 63% of the individuals could be classified as having bipolar disorder. The researchers then used text features and coded data from the EHR to generate automated algorithms that classified patients as likely to have bipolar disorder. It is important to note that they next conducted a validation study on a selected subset of cases, for which they compared the EHR-and algorithm-derived diagnoses to those made on the basis of direct diagnostic interview by clinicians using the Structured Clinical Interview for DSM-IV. A quite respectable 79% J85% of the patients electronically classified as having bipolar disorder also had the diagnosis on direct interview, while none of those classified as control subjects did. The authors are ultimately interested in using their method to identify samples for genetics studies of bipolar disorder. Their result represents an important step forward in the application of the big data approach to pinpointing genetic susceptibility variants in bipolar disorder.
