Results 231 to 240 of about 66,452 (290)

The Brain‐Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes

Movement Disorders, EarlyView.
Abstract Background Dystonia in children is a heterogeneous condition with variable response to deep brain stimulation (DBS). Brain‐age gap, a machine learning‐derived metric of structural deviation from norm, may capture signatures that differentiate underlying biotypes and predict outcomes.
Timur H. Latypov, Alexandre Berger, Manuela Mohareb, Karim Mithani, Sara Breitbart, Lindsey M. Vogt, Thiemo Dinger, Inge Meijer, Alexander G. Weil, Alfonso Fasano, Carolina Gorodetsky, George M. Ibrahim   +11 more
wiley   +1 more source

Development of a Practical Nomogram for Depression Risk Stratification in Older Adults With Hypertension and Diabetes: Retrospective Analysis of Data From the China Health and Retirement Longitudinal Study.

JMIR Med Inform
Peng T, Zhang Y, Miao R, Wang J.
europepmc   +1 more source

Development and validation of explainable machine learning models for the prediction of survival in patients with M1 breast cancer. [PDF]

Gland Surg
Jin L, Zhao Q, Fu S, Chao Z, Cao F, Wu J, Ma D, Zhu X, Zhang Y.   +8 more
europepmc   +1 more source

Diagnostic Value of Glycocalyx Shedding in Blood for Differentiating between Parkinson's Disease and Multiple System Atrophy

Movement Disorders, EarlyView.
Abstract Background Blood–brain barrier disruption is increasingly recognized in synucleinopathies, but the role of the endothelial glycocalyx (GLX) in Parkinson's disease (PD) and multiple system atrophy (MSA) remains unclear. Objectives The aim was to determine whether plasma GLX markers differ between PD, MSA, and healthy controls (HC), relate to ...
Jonas Folke, Brian DellaValle, Mathias Lindh Jørgensen, Phillip Bredahl Mogensen, Casper Hempel, Jørgen Rungby, Anne‐Mette Hejl, Annemette Løkkegaard, Lisette Salvesen, Sara Bech, Mette Møller, Erik Hvid Danielsen, Morten Blaabjerg, Matthias Bode, Tomasz Brudek, Susana Aznar   +15 more
wiley   +1 more source

Machine learning-based prediction of activities of daily living in patients with stroke and other acquired brain injuries. [PDF]

Medicine (Baltimore)
Omi C, Wada Y.
europepmc   +1 more source

Designing High‐Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning

Materials Genome Engineering Advances, EarlyView.
In this study, we developed an interpretable machine learning (ML) ensemble framework and, by integrating the VEC criterion with the proposed machine learning scoring parameter in the alloy composition screening process, successfully designed multiple CoCrFeNiMn‐based HEAs with TWIP/TRIP effects and without the BCC phase.
Shuai Nie, Yixuan He, Haoxiang Liu, Xudong Liu, Haifeng Wang, Ziqing He, Menghao Yang   +6 more
wiley   +1 more source

Health opportunity inequality in middle-aged and older adult cardiovascular and cerebrovascular patients. [PDF]

Front Public Health
Hu G, Zhao H, Yu Z, Liu X.
europepmc   +1 more source

Research on the Prediction of Coal Workers' Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study. [PDF]

JMIR Med Inform
Li H, Jia J, Shi X, Dong Y, Wang S, Cui Y, Hang W, Zhang D.   +7 more
europepmc   +1 more source

Smartphone addiction and temporomandibular disorders among university students: A machine learning based multiple regression analysis study. [PDF]

Medicine (Baltimore)
Güzel HÇ, Türkmen OB, Kurtoğlu A, Keçelioğlu S, Akçay B, Mutlu EK, Albasha N, Elkholi SM.   +7 more
europepmc   +1 more source

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation. [PDF]

Open Heart
Yim WY, Li Y, Hou J, Chen Y, Xiong T, Li C, Lai J, Peng Y, Geng B, Wu Y, Tong F, Wang Y, Dong N.   +12 more
europepmc   +1 more source