AIMC Topic: Longitudinal Studies
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Construction of disability risk prediction model for the elderly based on machine learning.
Scientific reports
May 9, 2025
The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing disability risk in older adults. Data from the 2018 and 2020 waves of the China Health and Retirement ...
Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.
BMC pregnancy and childbirth
May 8, 2025
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...
Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.
Schizophrenia bulletin
Mar 14, 2025
BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of ...
Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.
European heart journal
Mar 13, 2025
BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF r...
Machine learning-based risk assessment for cardiovascular diseases in patients with chronic lung diseases.
Medicine
Mar 7, 2025
The association between chronic lung diseases (CLDs) and the risk of cardiovascular diseases (CVDs) has been extensively recognized. Nevertheless, conventional approaches for CVD risk evaluation cannot fully capture the risk factors (RFs) related to ...
[Prediction of depression symptoms in seniors and analysis of influencing factors based on explainable machine learning].
Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
Feb 10, 2025
This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method. Based on entries from ...
Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.
Pharmacology research & perspectives
Feb 1, 2025
The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diabetes (T2D) in the U.S. In Japan, there is little evidence on the HbA1c-lowering effect of high-dose ...
Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learning.
PloS one
Jan 1, 2025
Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies and contributes to up to 26% of maternal deaths. Despite extensive clinical research, current predic...
Urban-rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.
Frontiers in public health
Jan 1, 2025
BACKGROUND: Falls among older adults are a significant challenge to global healthy aging. Identifying key factors and differences in fall risks, along with developing predictive models, is essential for differentiated and precise interventions in Chi...
Can the Use of Artificial Intelligence-Generated Content Bridge the Cancer Knowledge Gap? A Longitudinal Study With Health Self-Efficacy as a Mediator and Educational Level as a Moderator.
Cancer control : journal of the Moffitt Cancer Center
Jan 1, 2025
OBJECTIVES: The cancer knowledge gap represents a significant disparity in awareness and understanding of cancer-related information across different demographic groups. Leveraging Artificial Intelligence-Generated Content (AIGC) offers a promising a...
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