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. 2016 Nov 11;4(4):e37. doi: 10.2196/medinform.6328

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©Le Zheng, Yue Wang, Shiying Hao, Andrew Y Shin, Bo Jin, Anh D Ngo, Medina S Jackson-Browne, Daniel J Feller, Tianyun Fu, Karena Zhang, Xin Zhou, Chunqing Zhu, Dorothy Dai, Yunxian Yu, Gang Zheng, Yu-Ming Li, Doff B McElhinney, Devore S Culver, Shaun T Alfreds, Frank Stearns, Karl G Sylvester, Eric Widen, Xuefeng Bruce Ling. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 11.11.2016.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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The multidimensional scaling (MDS) plots of the training result. This analysis was aimed at detecting meaningful underlying dimensions, for example, 1 and 2, which allow the explanation of the observed similarities (distances) between the investigated subjects. The axes of the MDS plots represent no real sizes and thus were marked as dimension 1 and dimension 2 without units. The red dots and blue triangles, indicating the positive and negative samples, were clearly separated. The “false positives” are circled in the plot. Chart reviews showed that these were notes with a genuine diagnosis of diabetes mellitus.