Machine and deep learning for longitudinal biomedical data: a review of methods and applications
A Cascarano, J Mur-Petit… - Artificial Intelligence …, 2023 - Springer
Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical
field, as many diseases have a complex and multi-factorial time-course, and start to develop …
field, as many diseases have a complex and multi-factorial time-course, and start to develop …
Transformer networks for trajectory forecasting
Most recent successes on forecasting the people motion are based on LSTM models and all
most recent progress has been achieved by modelling the social interaction among people …
most recent progress has been achieved by modelling the social interaction among people …
Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challenging machine
learning task. Existing DPM algorithms neglect temporal dependencies among …
learning task. Existing DPM algorithms neglect temporal dependencies among …
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
Modelling the spatiotemporal dynamics in the activity of neural populations while also
enabling their flexible inference is hindered by the complexity and noisiness of neural …
enabling their flexible inference is hindered by the complexity and noisiness of neural …
Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm
J Albright… - Alzheimer's & Dementia …, 2019 - Elsevier
Abstract Introduction There is a 99.6% failure rate of clinical trials for drugs to treat
Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily …
Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily …
Rethinking modeling Alzheimer's disease progression from a multi-task learning perspective with deep recurrent neural network
Alzheimer's disease (AD) is a severe neurodegenerative disorder that usually starts slowly
and progressively worsens. Predicting the progression of Alzheimer's disease with …
and progressively worsens. Predicting the progression of Alzheimer's disease with …
Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects
M Velazquez, Y Lee… - Plos one, 2021 - journals.plos.org
Alzheimer's Disease (AD) conversion prediction from the mild cognitive impairment (MCI)
stage has been a difficult challenge. This study focuses on providing an individualized MCI …
stage has been a difficult challenge. This study focuses on providing an individualized MCI …
Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis
Background Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration
disorder with varied rates of deterioration, either between subjects or within different stages …
disorder with varied rates of deterioration, either between subjects or within different stages …
Deep switching auto-regressive factorization: Application to time series forecasting
We introduce deep switching auto-regressive factorization (DSARF), a deep generative
model for spatio-temporal data with the capability to unravel recurring patterns in the data …
model for spatio-temporal data with the capability to unravel recurring patterns in the data …
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission
A Zarghani - arXiv preprint arXiv:2406.19980, 2024 - arxiv.org
Diabetes mellitus is a chronic metabolic disorder that has emerged as one of the major
health problems worldwide due to its high prevalence and serious complications, which are …
health problems worldwide due to its high prevalence and serious complications, which are …