AIMC Topic: Disease Progression
Clear Filters Showing 341 to 350 of 750 articles
Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T-weighted Contrast-enhanced Imaging.
BMC medical imaging
Feb 3, 2021
BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate th...
Screening of Alzheimer's disease by facial complexion using artificial intelligence.
Aging
Jan 25, 2021
Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered. This study aimed to examine whether artificial intelligence (AI) could dis...
An Interpretable Machine Learning Survival Model for Predicting Long-term Kidney Outcomes in IgA Nephropathy.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
IgA nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes is important for clinical decision-making. As right-censored patients become common during the long-term follow-up, either excluding these pati...
Cognitive and MRI trajectories for prediction of Alzheimer's disease.
Scientific reports
Jan 22, 2021
The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI databa...
Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.
BMC biology
Jan 13, 2021
BACKGROUND: Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenes...
Modular machine learning for Alzheimer's disease classification from retinal vasculature.
Scientific reports
Jan 8, 2021
Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do no...
Recurrent disease progression networks for modelling risk trajectory of heart failure.
PloS one
Jan 6, 2021
MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-di...
Deep learning detection of informative features in tau PET for Alzheimer's disease classification.
BMC bioinformatics
Dec 28, 2020
BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and ...
Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.
European journal of cancer (Oxford, England : 1990)
Dec 26, 2020
BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient ...
Adult
Survival Rate
Breast Neoplasms
Middle Aged
Female
Aged
Aged, 80 and over
Young Adult
Receptor, ErbB-2
Receptors, Estrogen
Receptors, Progesterone
Disease Progression
Antineoplastic Combined Chemotherapy Protocols
Follow-Up Studies
Natural Language Processing
Machine Learning
Humans
Prognosis
Retrospective Studies
Electronic Health Records
Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.
Ophthalmology
Dec 25, 2020
PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical da...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31088
- Neural Networks, Computer 29743
- Algorithms 26874
- Deep Learning 26387
- Artificial Intelligence 24329
- Robotics 13315
- Image Processing, Computer-Assisted 9536
- Retrospective Studies 9125
- Magnetic Resonance Imaging 6192
- Reproducibility of Results 5600
- Tomography, X-Ray Computed 4772
- Support Vector Machine 4692
- Computational Biology 4243
- Brain 4053
- Computer Simulation 3885
Recent Journals
- Scientific reports 6015
- Sensors (Basel, Switzerland) 4794
- PloS one 4029
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1864
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1133
- Journal of chemical information and modeling 1003
- Nature communications 952
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 862
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 828