AIMC Topic: Machine Learning

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Machine learning-based prediction of disability risk in geriatric patients with hypertension for different time intervals.

Archives of gerontology and geriatrics
BACKGROUND: The risk of disability in older adults with hypertension is substantially high, and prediction of disability risk is crucial for subsequent management. This study aimed to construct prediction models of disability risk for geriatric patie...

Artificial Intelligence and Big Data Science in Neurocritical Care.

Critical care clinics
In recent years, the volume of digitalized web-based information utilizing modern computer-based technology for data storage, processing, and analysis has grown rapidly. Humans can process a limited number of variables at any given time. Thus, the de...

Artificial Intelligence in Oral and Maxillofacial Surgery Education.

Oral and maxillofacial surgery clinics of North America
Artificial intelligence has become ubiquitous with modern technology. Digital transformations are occurring in every field including medicine, surgery, and education. Computers and computer programs are getting sophisticated to form neural networks g...

Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography.

Pediatric cardiology
BACKGROUND: Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and c...

Memory Visualization-Based Malware Detection Technique.

Sensors (Basel, Switzerland)
Advanced Persistent Threat is an attack campaign in which an intruder or team of intruders establishes a long-term presence on a network to mine sensitive data, which becomes more dangerous when combined with polymorphic malware. This type of malware...

Artificial intelligence in hematological diagnostics: Game changer or gadget?

Blood reviews
The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have sho...

Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS).

Journal of exposure science & environmental epidemiology
BACKGROUND: Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus.

Multi-omics disease module detection with an explainable Greedy Decision Forest.

Scientific reports
Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form ...

Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan.

Scientific reports
The development of computer-aided detection (CAD) using artificial intelligence (AI) and machine learning (ML) is rapidly evolving. Submission of AI/ML-based CAD devices for regulatory approval requires information about clinical trial design and per...