AIMC Topic: Aged

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Predicting geriatric environmental safety perception assessment using LightGBM and SHAP framework.

Scientific reports
Global population aging highlights the need to understand how the elderly perceive safety in urban public spaces. This study used image semantic segmentation to identify key visual elements from panoramic images. A dataset was created by combining ma...

Gut microbiota and SCFAs improve the treatment efficacy of chemotherapy and immunotherapy in NSCLC.

NPJ biofilms and microbiomes
The role of gut dysbiosis in shaping immunotherapy responses is well-recognized, yet its effect on the therapeutic efficacy of chemotherapy and immunotherapy combinations remains poorly understood. We analyzed gut microbiota in non-small cell lung ca...

Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.

Annals of family medicine
PURPOSE: Factors influencing missed appointments are complex and difficult to anticipate and intervene against. To optimize appointment adherence, we aimed to use personalized machine learning and big data analytics to predict the risk of and contrib...

Enhancing central visual field loss representation with a hybrid unsupervised approach.

International ophthalmology
PURPOSE: To effectively represent central visual field (VF) loss for individual patients using a hybrid unsupervised approach.

Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.

PloS one
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of mortality and morbidity in China and worldwide while we are lacking in validated primary prevention model specifically for Chinese. To identify CVD high-risk individuals for early inter...

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...

A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.

Brain research
Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensiv...

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 ...