Results 181 to 190 of about 77,058 (245)
Post-selection inference for causal effects after causal discovery. [PDF]
Chang TH, Guo Z, Malinsky D.
europepmc +1 more source
Abstract Background Adolescence is marked by increased vulnerability to sleep disturbances and mood disorders. Understanding how day‐to‐day changes in sleep and mood are linked within the same individual is crucial for clarifying sleep's role in emerging internalizing disorders. However, the extent to which an adolescent's fluctuations in sleep predict
Konstantin Drexl +4 more
wiley +1 more source
Deep learning radiomics model of epicardial adipose tissue for predicting postoperative atrial fibrillation after lung lobectomy in lung cancer patients. [PDF]
Liu Z +8 more
europepmc +1 more source
Abstract Background Sleep disturbances are widely considered to be a transdiagnostic feature of common behavioural and emotional difficulties in childhood, yet most studies treat sleep as a single construct. Where studies have explored specific sleep problems to psychopathology in children, these tend to only include behavioural or emotional ...
Alina A. Marinca +4 more
wiley +1 more source
Abstract Evidence regarding the prognosis of thyroid carcinoma is heterogeneous, ranging from age effects and nodal burden metrics, such as lymph node ratio (LNR) and log odds of positive nodes (LODDS), to preoperative imaging models comprising ultrasound, CEUS, and radiomics.
Mennatallah Sherif +2 more
wiley +1 more source
Enhancing Omics Analyses Through Coalitional Games and Shapley Values. [PDF]
Vargas E, de la Torre I, Esteban FJ.
europepmc +1 more source
ABSTRACT Background Upper‐airway morphology changes during breathing can be captured with cine 4D MRI. Active‐learning nnU‐Net reduces manual labeling while maintaining accuracy. Purpose For automatic upper airway segmentation on free‐breathing cine 4D MRI using active learning and quantifying dynamic changes under two mouth positions.
Cheng‐Yang Yu +7 more
wiley +1 more source
Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data. [PDF]
Gouda HF, Abdallah FDM.
europepmc +1 more source

