首页|Heinrich-Heine-University Dusseldorf Reports Findings in Anxiety Disorders (Pred iction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets)
Heinrich-Heine-University Dusseldorf Reports Findings in Anxiety Disorders (Pred iction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Mental Health Diseases and Conditions - Anxiety Disorders is the subject of a report. According to new s reporting from Dusseldorf, Germany, by NewsRx journalists, research stated, “D epressive symptoms are rising in the general population, but their associated fa ctors are unclear. Although the link between sleep disturbances and depressive s ymptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure.” The news correspondents obtained a quote from the research from Heinrich-Heine-U niversity Dusseldorf, “Using three population-based datasets (N = 1813), we trai ned the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and fu nctional) measurements on DSS, then we tested our models’ performance in two ind ependent datasets (N = 378, N = 334) to test the generalizability of our finding s. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66 ). In addition, we performed a mediation analysis to identify the role of anxiet y and brain measurements on the sleep quality and DSS association. Sleep quality could predict individual DSS (r = 0.43, R = 0.18, rMSE = 2.73), and adding anxi ety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar re sults. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS. Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model’s performance but also mediated the link between sleep quality and DSS. The study is supported by Helmholtz Imaging Platform gran t (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundat ion)-Project-ID 431549029-SFB 1451, the programme ‘Profilbildung 2020’ (grant no .”
DusseldorfGermanyEuropeAnxietyAn xiety DisordersCyborgsEmerging TechnologiesHealth and MedicineMachine Le arningMental Health Diseases and Conditions
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