AIMC Topic: Machine Learning

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A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: current status and future directions.

BMC psychiatry
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition affecting approximately 3.5% of the global population, with diagnosis on average delayed by 7.1 years or often confounded with other psychiatric disorders. Advances in electroen...

Machine learning-based assessment of land use change effects on land surface temperature fluctuations in Ho Chi Minh city, Vietnam.

Environmental monitoring and assessment
Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, ...

Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.

JMIR research protocols
BACKGROUND: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effe...

Kinship verification via correlation calculation-based multi-task learning.

PloS one
Previous studies have demonstrated that metric learning approaches yield remarkable performance in the field of kinship verification. Nevertheless, a prevalent limitation of most existing methods lies in their over-reliance on learning exclusively fr...

Long sequence temporal knowledge tracing for student performance prediction via integrating LSTM and informer.

PloS one
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. Ho...

Construct prediction models for low muscle mass with metabolic syndrome using machine learning.

PloS one
BACKGROUND: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posin...

Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events.

PloS one
Capturing the dynamic changes in patients' internal states as they approach death due to fatal diseases remains a major challenge in understanding individual pathologies and improving end-of-life care. However, existing methods primarily focus on spe...

Comprehensive analysis of disulfidptosis-related genes in pulmonary hypertension through machine learning and immune infiltration: Spotlight on USP32 and ZNF655 as key regulators.

PloS one
BACKGROUND: Disulfidptosis, a novel cellular death manner, has yet to be fully explored within the context of pulmonary arterial hypertension (PAH). This study aims to identify genes implicated in PAH that are involved in disulfidptosis.

Advances and Challenges in Machine Learning for RNA-Small Molecule Interaction Modeling: Review.

Journal of chemical theory and computation
RNA plays a pivotal role in biological processes such as gene expression regulation and protein synthesis. Targeting RNA with small molecules offers a novel therapeutic strategy for various diseases by directly modulating these processes. However, th...

Hollow polydopamine for the enhanced ultrasensitive dual-modal immunochromatographic detection of Salmonella typhimurium in dairy products via machine learning.

Mikrochimica acta
Salmonella typhimurium (S. typhimurium) A dual-mode colorimetric/photothermal immunochromatographic strip (ICS) employing hollow polydopamine nanoparticles (h-PDA) is reported for the ultrasensitive detection of Salmonella typhimurium (S. typhimurium...