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  • 标题:Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales
  • 本地全文:下载
  • 作者:Hanna Christiansen ; Mira-Lynn Chavanon ; Oliver Hirsch
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2020
  • 卷号:10
  • 期号:1
  • 页码:1-10
  • DOI:10.1038/s41598-020-75868-y
  • 出版社:Springer Nature
  • 摘要:A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners’ Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of .80; precision (positive predictive value) ranged between .78 (gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD.
  • 其他摘要:Abstract A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners’ Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of .80; precision (positive predictive value) ranged between .78 (gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD.
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