A study in the Journal of Translational Medicine finds that in chronic fatigue syndrome, there are subgroups of patients with a similar pattern of symptoms. These symptom-based clusters might help develop tailored treatment options, suggest the authors.
Participants reported the frequency and severity of 79 symptoms related to the illness over the past 6 months. Frequency of symptoms was rated on a 5-point Likert scale: 0 = none of the time, 1 = a little of the time, 2 = about half the time, 3 = most of the time, and 4 = all of the time. Similarly, severity of symptoms was rated on a 5-point Likert scale: 0 = symptom not present, 1 = mild, 2 = moderate, 3 = severe, and 4 = very severe.
Symptom scores were analyzed in two ways: a composite variable was created by averaging the frequency and severity scores of each symptom and multiplying it by 25; the composite score of each symptom ranged from 0 to 100 points.
Severity and frequency scores of the 79 measured symptoms were used. Therefore each participant had 158 features. Clustering was performed on MATLAB , following its default SOM setting, except for the number of iterations for training the SOM, which was changed to 1000 []. The default random number generation of MATLAB was used to initialize all competitive units of the SOM, meaning that with the same input and SOM settings, the results are always the same.
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