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 …
Remote smartphone monitoring of Parkinson's disease and individual response to therapy
Remote health assessments that gather real-world data (RWD) outside clinic settings
require a clear understanding of appropriate methods for data collection, quality …
require a clear understanding of appropriate methods for data collection, quality …
[HTML][HTML] Radiomic and genomic machine learning method performance for prostate cancer diagnosis: systematic literature review
Background Machine learning algorithms have been drawing attention at the joining of
pathology and radiology in prostate cancer research. However, due to their algorithmic …
pathology and radiology in prostate cancer research. However, due to their algorithmic …
Subject-specific cognitive workload classification using EEG-based functional connectivity and deep learning
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other
real-time and high-risk situations. Neuroimaging techniques have long been used for …
real-time and high-risk situations. Neuroimaging techniques have long been used for …
Machine Learning in the Parkinson's disease smartwatch (PADS) dataset
J Varghese, A Brenner, M Fujarski, CM van Alen… - npj Parkinson's …, 2024 - nature.com
The utilisation of smart devices, such as smartwatches and smartphones, in the field of
movement disorders research has gained significant attention. However, the absence of a …
movement disorders research has gained significant attention. However, the absence of a …
Determination of Gas–Oil minimum miscibility pressure for impure CO2 through optimized machine learning models
Minimum miscibility pressure (MMP) is one of the most important parameters for designing
CO 2 enhanced oil recovery (EOR) and associated storage in depleted oil reservoirs. The …
CO 2 enhanced oil recovery (EOR) and associated storage in depleted oil reservoirs. The …
[HTML][HTML] Machine learning-based diabetic neuropathy and previous foot ulceration patients detection using electromyography and ground reaction forces during gait
Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves
alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is …
alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is …
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
Consumer wearables and sensors are a rich source of data about patients' daily disease
and symptom burden, particularly in the case of movement disorders like Parkinson's …
and symptom burden, particularly in the case of movement disorders like Parkinson's …
[HTML][HTML] A machine learning approach for semi-automatic assessment of IADL dependence in older adults with wearable sensors
FM Garcia-Moreno, M Bermudez-Edo… - International journal of …, 2022 - Elsevier
Abstract Background and Objective The assessment of dependence in older adults currently
requires a manual collection of data taken from questionnaires. This process is time …
requires a manual collection of data taken from questionnaires. This process is time …
Identifying the signature of prospective motor control in children with autism
Failure to develop prospective motor control has been proposed to be a core phenotypic
marker of autism spectrum disorders (ASD). However, whether genuine differences in …
marker of autism spectrum disorders (ASD). However, whether genuine differences in …