AIMC Topic: Disease Progression
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Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2025
The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the pro...
Predicting conversion in cognitively normal and mild cognitive impairment individuals with machine learning: Is the CSF status still relevant?
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 30, 2025
INTRODUCTION: Machine learning (ML) helps diagnose the mild cognitive impairment-Alzheimer's disease (MCI-AD) spectrum. However, ML is fed with data unavailable in standard clinical practice. Thus, we tested a novel multi-step ML approach to predict ...
A Deep Learning Tool for Minimum Joint Space Width Calculation on Antero-posterior Knee Radiographs.
The Journal of arthroplasty
Jan 27, 2025
BACKGROUND: Minimum joint space width (mJSW) is an important continuous quantitative metric of osteoarthritis progression in the knee. The purpose of this study was to develop an automated measurement algorithm for mJSW in the medial and lateral comp...
An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 27, 2025
INTRODUCTION: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO clinical cancer informatics
Jan 24, 2025
PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evalu...
Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.
Genome research
Jan 22, 2025
Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in humans. However, individual cells in patient-derived tissues are in different pathological stages, and henc...
Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation.
International journal of chronic obstructive pulmonary disease
Jan 20, 2025
BACKGROUND: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Time Factors
Support Vector Machine
Predictive Value of Tests
Functional Status
Respiratory Function Tests
Prospective Studies
Speech Acoustics
Prognosis
Speech Production Measurement
Female
Humans
Reproducibility of Results
Disease Progression
Voice Quality
Lung
Severity of Illness Index
Aged
Speech
Pulmonary Disease, Chronic Obstructive
Male
Middle Aged
A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.
Scientific reports
Jan 18, 2025
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. a...
Machine learning-based prediction of illness course in major depression: The relevance of risk factors.
Journal of affective disorders
Jan 14, 2025
BACKGROUND: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are stil...
Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.
Annals of medicine
Jan 13, 2025
OBJECTIVE: We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.
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