AIMC Topic: Aged, 80 and over
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A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.
Scientific reports
Mar 11, 2025
An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric me...
Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand.
Scientific reports
Mar 11, 2025
This study aims to construct a prediction model for the demand for medical and daily care services of the elderly and to explore the factors that affect the demand for medical and daily care services of the elderly. In this study, a questionnaire sur...
Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology.
Journal of Korean medical science
Mar 10, 2025
BACKGROUND: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment,...
A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 10, 2025
BACKGROUND: To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).
Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.
The journal of prevention of Alzheimer's disease
Mar 8, 2025
BACKGROUND: Integrating machine learning with medical records offers potential for early detection of Alzheimer's disease (AD), enabling timely interventions.
Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.
International journal of molecular sciences
Mar 8, 2025
Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer's disease (AD). This study...
Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
Mar 7, 2025
UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AU...
An explainable machine learning-based prediction model for sarcopenia in elderly Chinese people with knee osteoarthritis.
Aging clinical and experimental research
Mar 7, 2025
BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disease that leads to loss of muscle mass and function, resulting in adverse health outcomes such as falls, functional decline, and death. Knee osteoarthritis (KOA) is a common chro...
Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.
Japanese journal of radiology
Mar 5, 2025
PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs...
Added value of artificial intelligence for the detection of pelvic and hip fractures.
Japanese journal of radiology
Mar 5, 2025
PURPOSE: To assess the added value of artificial intelligence (AI) for radiologists and emergency physicians in the radiographic detection of pelvic fractures.
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