ArticleSleep & breathing = Schlaf & Atmung2024
Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.
Article in Sleep & breathing = Schlaf & Atmung, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).BMC psychiatry · 2026Article
- Machine learning approached a 14-item shortened version of the Positive And Negative Sleep Appraisal Measure (PANSAM-14).Sleep & breathing = Schlaf & Atmung · 2026Article
- The Dysfunctional Self-Focus Attributes Scale-7 (DSAS-7): A Machine Learning-based Development of a Shortened Version of the DSAS.Journal of medical systems · 2026Article
- Concurrent utilization of pregabalin and Zhengqing fengtongning sustained-release tablets in managing postherpetic neuralgia and its effect on pain relief, VAS scores, Insomnia Severity Index (ISI), SF-36, serum interleukin-17 (IL-17), prostaglandin E2 (PGE2).Postepy dermatologii i alergologii · 2026Article
- Cancer-related Dysfunctional Beliefs and Attitude about Sleep-6 (C-DBAS-6): a practical and accurate shortened version using XGBoost and SymScore.Sleep and biological rhythms · 2025Article
- A cross-sectional study of the current status of psychological health and its correlation with academic performance in medical students: taking medical students in a medical university in China as examples.Frontiers in psychiatry · 2025Article
- Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.NPJ digital medicine · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundThe Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder.
methodsWe collected a sample of 800 responses from the EMBRAIN survey system. Based on the responses, seven items were grouped based on the similarity of their response using exploratory factor analysis (EFA). The most representative item within each group was selected by using eXtreme Gradient Boosting (XGBoost).
resultsBased on the selected three key items, maintenance of sleep, interference with daily function, and concerns about sleep problems, we developed a data-driven shortened questionnaire of ISI, ISI-3 m (machine learning). ISI-3 m achieved the highest coefficient of determination (
conclusionAs 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.
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Registered trials
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