AIMC Topic: Longitudinal Studies
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Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.
Journal of medical Internet research
Aug 21, 2025
BACKGROUND: Depression is highly recurrent and heterogeneous. The unobtrusive, continuous collection of mobile sensing data via smartphones and wearable devices offers a promising approach to monitor and predict individual depression trajectories, di...
Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: A modeling study.
PLoS medicine
Aug 21, 2025
BACKGROUND: Knee osteoarthritis (KOA) worsens both structurally and symptomatically, yet no model predicts KOA progression using Magnetic Resonance Image (MRI) radiomics and biomarkers. This study aimed to develop and test the longitudinal Load-Beari...
Advancing fall risk prediction in older adults with cognitive frailty: A machine learning approach using 2-year clinical data.
PloS one
Aug 21, 2025
Falls are a critical concern in older adults with cognitive frailty (CF). However, previous studies have not fully examined whether machine learning models can predict falls in older individuals with CF. The 2-year longitudinal data set from the Kore...
Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment.
Nature communications
Aug 19, 2025
Longitudinal serological proteomic dynamics during antiviral therapy (AVT) in chronic hepatitis B (CHB) patients with liver fibrosis remain poorly characterized. Here, using four-dimensional data-independent acquisition mass spectrometry (4D-DIA-MS),...
Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.
Alzheimer's research & therapy
Aug 7, 2025
BACKGROUND: Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical managem...
A Machine Learning-Based Prognostication Model Enhances Prediction of Early Hepatic Encephalopathy in Patients With Noncancer-Related Cirrhosis: Multicenter Longitudinal Cohort Study in Taiwan.
JMIR medical informatics
Aug 6, 2025
BACKGROUND: Hepatic encephalopathy (HE) contributes significantly to mortality among patients with liver cirrhosis. Early prediction of HE is essential for clinical decision-making, yet remains challenging-particularly in noncancer-related cirrhosis ...
Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Aug 6, 2025
The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1: = 17...
Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.
Annals of family medicine
Jul 28, 2025
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...
A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study.
Scientific reports
Jul 24, 2025
This study aims to identify depressive risks in elderly individuals with subjective cognitive decline (SCD) and develop a predictive model using machine learning algorithms to enable timely interventions.Data from the 2015 and 2018 waves of the China...
A longitudinal cohort study uncovers plasma protein biomarkers predating clinical onset and treatment response of rheumatoid arthritis.
Nature communications
Jul 21, 2025
Rheumatoid arthritis (RA) is a systemic inflammatory condition posing challenges in identifying biomarkers for onset, severity and treatment responses. Here we investigate the plasma proteome in a longitudinal cohort of 278 RA patients, alongside 60 ...
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