Abstract
A multidimensional-scaling approach is proposed to analyze the main symptoms of T2DM. Based on 200 Type 2 diabetes patients' electronic medical records, the terms which were used to described symptoms in the records and their co-occurring query terms were analyzed. A distanced-based similarity measure was used to calculate the proximity of terms to one and another based on their co-occurrences in the 200 medical records. After the calculation of the distance between each two keywords, a visual clustering of groups of terms was conducted. Each terms distribution within each visual configuration showed the most common symptoms of Type 2 diabetes such as Foam in Urine, Intermittent Dizziness, Hyperlipemia, Feeble, Diuresis etc; however it also showed some hidden relations behind our cognition. © 2014 Springer International Publishing.
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CITATION STYLE
Meng, X., & Yang, J. J. (2014). Visual analysis for type 2 diabetes mellitus - Based on electronic medical records. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8549 LNCS, pp. 160–170). Springer Verlag. https://doi.org/10.1007/978-3-319-08416-9_17
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