AI Medical Compendium Topic:
Disease Progression

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A machine learning approach to triaging patients with chronic obstructive pulmonary disease.

PloS one
COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacer...

Predictions of ocular changes caused by diabetes in glaucoma patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This paper builds different neural network models with simple topologies, having one or two hidden layers which were subsequently employed in the prediction of ocular changes progression in patients with diabetes associated ...

Reconstructing cell cycle and disease progression using deep learning.

Nature communications
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progressi...

A machine-learning based analysis for the recognition of progressive central hypovolemia.

Physiological measurement
OBJECTIVE: Traditional patient monitoring during surgery includes heart rate (HR), blood pressure (BP) and peripheral oxygen saturation. However, their use as predictors for central hypovolemia is limited, which may lead to cerebral hypoperfusion. Th...

Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

Journal of medical engineering & technology
Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection ...

AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure.

NeuroImage
Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an uns...

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.

BMC medical informatics and decision making
BACKGROUND: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patient...

The Economic Burden of ACPA-Positive Status Among Patients with Rheumatoid Arthritis.

Journal of managed care & specialty pharmacy
BACKGROUND: Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA di...

Identifying incipient dementia individuals using machine learning and amyloid imaging.

Neurobiology of aging
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the co...