AIMC Topic: Machine Learning

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A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

Medical & biological engineering & computing
The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques,...

Using machine learning to identify environmental factors that collectively determine microbial community structure of activated sludge.

Environmental research
Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we em...

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Annals of surgical oncology
BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening ...

A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.

European journal of internal medicine
BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a condition characterized by the abnormal accumulation of hepatic lipids that is clos...

Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission.

Computers in biology and medicine
BACKGROUND: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as...

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Multiple sclerosis and related disorders
Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though M...

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Journal of chemical information and modeling
Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to...

Information disclosure, multifaceted collaborative governance, and carbon total factor productivity-An evaluation of the effects of the 'environmental information disclosure pilot' policy based on double machine learning.

Journal of environmental management
As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information disclosure pilot policy, can and how to affect th...

Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.

Sensors (Basel, Switzerland)
Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagno...