AIMC Topic: Adult

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Enhancing surface electromyographic signal recognition accuracy for trans-radial amputees using broad learning systems.

Biomedical physics & engineering express
Gesture recognition based on surface electromyography (sEMG) plays a crucial role in human-computer interaction. By analyzing sEMG signals generated from residual forearm muscle activity in trans-radial amputees, it is possible to predict their hand ...

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one
In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and i...

Understanding Physical Activity Facilitated by a Single Session of Robotic Walking for Children and Small Adults Living With Severe Mobility Impairments.

Journal of physical activity & health
BACKGROUND: Physical activity has many benefits but can be hard to achieve for people living with severe mobility impairments. Robotic walking may be an effective way for these individuals to achieve physical activity.

Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort.

Arthritis research & therapy
BACKGROUND: The variability in treatment response in people with rheumatoid arthritis (RA) warrants the prediction of patients at high risk of treatment failure. Identification of biomarkers linked to clinical remission in RA is currently a challenge...

Machine learning-based dynamic CEA trajectory and prognosis in gastric cancer.

BMC cancer
BACKGROUND: Static carcinoembryonic antigen (CEA) levels are well‑established prognostic markers in patients with gastric cancer, but the significance of their dynamic trajectories over time has rarely been reported.

Application of metabolomics and MCDM approach in developing a novel strategy for disease diagnosis: A case study in Primary Sjögren's Syndrome.

Journal of pharmaceutical and biomedical analysis
Primary Sjögren's Syndrome (pSS) is a complex autoimmune disease with an unclear etiology. Due to the lack of a single diagnostic gold standard, multidisciplinary and invasive examinations are often required for pSS, underscoring the urgent need for ...

Machine learning combine with nomogram to guide the establishment of endoscopic assistant system for gasless transaxillary endoscopic thyroidectomy.

Annals of medicine
OBJECTIVE: To explore the influence related factors of endoscopic assistant in gasless transaxillary endoscopic thyroidectomy by using machine learning and nomogram, and construct an endoscopic assistant system.

Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.

Annals of medicine
BACKGROUND: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear.

Unraveling the sensory metabolome of blueberries: An integrated metabolomics and machine learning approach across cultivars and geographical origins.

Food chemistry
Consumer-driven blueberry quality improvement requires a deeper understanding of how metabolic composition influences sensory perception. This study integrates untargeted metabolomics and machine learning to identify biomarker metabolites shaping sen...

Leveraging readily available clinical data with machine learning to predict first-line immunotherapy outcomes in non-small cell lung cancer.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitors (ICIs) are essential first-line treatments for recurrent or metastatic non-small cell lung cancer (NSCLC). However, predicting their effectiveness and the occurrence of immunotherapy-related adverse events (ir...