AIMC Topic: Aged

Clear Filters Showing 221 to 230 of 14227 articles

Functional connectivity between non-motor and motor networks predicts motor recovery changes after stroke.

Scientific reports
Stroke impairs limb motor function, which affects patients' quality of life and imposes economic burdens. Early prediction of motor recovery is essential for guiding treatment and rehabilitation. While the corticospinal tract is a known biomarker, th...

Development and validation of the risk stratification based on deep learning and radiomics to predict survival of advanced cervical cancer.

Scientific reports
Advanced cervical cancer (aCC) is associated with a poor prognosis. This study aimed to develop and validate a deep learning-based risk stratification model to predict overall survival in aCC patients using pre-treatment CT images. A total of 396 pat...

Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.

BMJ open
OBJECTIVES: Type 2 diabetes (T2D) is a complex disease with a heterogeneous clinical presentation. Recently, five distinct clusters of T2D have been identified in the Emirati population of long-standing T2D with complications. This study aimed to val...

Worse survival despite indolent features for triple-negative invasive lobular carcinoma: a Swedish nationwide registry-based study.

Breast cancer research and treatment
PURPOSE: To evaluate differences in clinical outcomes, treatments received, recurrence, and sociodemographic characteristics in patients with triple-negative breast cancer (TNBC) classified as invasive lobular carcinoma (TNBC-ILC) or invasive carcino...

Development and validation of a machine learning-based prognostic model for gastric cancer: a multicenter retrospective study.

Langenbeck's archives of surgery
BACKGROUND: Machine learning has emerged as a promising tool for survival prediction in various diseases; however, its application and external validation in real-world gastric cancer populations remain limited.

[Suicide epidemiology in the province of Málaga (Spain) [2024]: a retrospective analysis using machine learning techniques].

Revista espanola de salud publica
OBJECTIVE: Suicide constitutes a Public Health challenge whose demographic and geographical heterogeneity demands targeted interventions. The paucity of subnational studies integrating advanced data analytics restricts the precise identification of r...

Predicting the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures using machine learning algorithms.

PloS one
OBJECTIVE: To construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinic...

Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.

Renal failure
Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel tw...

Earlier prediction of Parkinson's disease using cross non-decimated wavelet transform and machine learning algorithm.

Scientific reports
Parkinson's disease (PD) is a brain disorder, that affects a person's body movement causing stiffness, shaking and imbalance. Earlier detection of PD is a challenging task for researchers. In this paper, earlier detection of PD is performed using the...

Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.

Scientific reports
Proximal junctional failure (PJF) is a significant mechanical complication following corrective surgery for adult spinal deformity (ASD), often resulting in structural failure at the uppermost instrumented vertebra and necessitating revision surgery....