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
palliative care
high throughput
high resolution
big data
combination therapy
deep learning
mass spectrometry
replacement therapy