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Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.

Hyeontae JoMyna LimHong Jun JeonJunseok AhnSaebom JeonJae Kyoung KimSeockhoon Chung
Published in: Sleep & breathing = Schlaf & Atmung (2024)
As ISI-3 m is a highly accurate shortened version of the ISI, it allows clinicians to efficiently screen for insomnia and observe variations in the condition throughout the treatment process. Furthermore, the framework based on the combination of EFA and XGBoost developed in this study can be utilized to develop data-driven shortened versions of the other questionnaires.
Keyphrases
  • machine learning
  • psychometric properties
  • sleep quality
  • high throughput
  • palliative care
  • depressive symptoms
  • deep learning
  • combination therapy
  • physical activity
  • big data
  • mass spectrometry