How Artificial Intelligence Is Uncovering Distinct Insomnia Phenotypes.

Authors

Ahmed S BaHammam

Published in

Sleep medicine clinics. Volume 21. Issue 4. Pages 509-519. Epub Sep 29, 2026.

Abstract

Insomnia is diagnosed as a single disorder. However, patients differ widely in biology, prognosis, and treatment response. Historical attempts to define subtypes failed on reliability and validity, leading current classifications to collapse them into one diagnosis. Data-driven methods are now recovering distinct phenotypes: trait-based subtypes from latent class analysis, an objective short-sleep phenotype tied to hyperarousal and cardiometabolic risk, actigraphic signatures separating acute from chronic insomnia, and large language models that identify cases in clinical records. These phenotypes carry prognostic and therapeutic weight, though prospective validation is still needed before phenotype-guided care can enter routine practice.

PMID:
42977360
Bibliographic data and abstract were imported from PubMed on 04 Nov 2026.

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