AIMC Topic: Machine Learning

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Evaluation of cerebral blood flow after subarachnoid hemorrhage using near-field coupling and machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundBedside continuous monitoring of cerebral blood flow (CBF) has significant implications in guiding individualized management and improving the prognosis of subarachnoid hemorrhage (SAH).ObjectiveThis study established a CBF monitoring syste...

How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant.

Water research
Wastewater treatment plants (WWTPs) generate vast amounts of water quality, operational, and biological data. The potential of these big data, particularly through machine learning (ML), to improve WWTP management is increasingly recognized. However,...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Aging cell
Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This study investigated APOE4 proteome changes in people with AD, mild cognitive impairment, and no impairme...

Interpretation of machine learning-based prediction models and functional metagenomic approach to identify critical genes in HBCD degradation.

Journal of hazardous materials
Hexabromocyclododecane (HBCD) poses significant environmental risks, and identifying HBCD-degrading microbes and their enzymatic mechanisms is challenging due to the complexity of microbial interactions and metabolic pathways. This study aimed to ide...

Identifying potential medical aid beneficiaries using machine learning: A Korean Nationwide cohort study.

International journal of medical informatics
OBJECTIVE: To identify potential medical aid beneficiaries using demographic and medical history of individuals and analyzing important features qualitatively.

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.

CPT: pharmacometrics & systems pharmacology
We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post-progression survival (PPS)-the duration from the time of document...

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.

Neurologia medico-chirurgica
This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the type of anticoagulation therapy in geriatric patients with traumatic brain injury. A retrospective ana...

Machine Vision with a CMOS-Based Hyperspectral Imaging Sensor Enables Sensing Meat Freshness.

ACS sensors
Imaging spectral information of materials and analysis of its properties have become an intriguing tool for consumer electronics used for food inspection, beauty care, etc. Those sensory physical quantities are difficult to quantify. Hyperspectral im...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Mathematical biosciences and engineering : MBE
Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis re...

Development of a short form of the Geriatric Depression Scale-30 based on item response theory and the RiskSLIM algorithm.

General hospital psychiatry
Recently, methods of quickly and accurately screening for geriatric depression have attracted substantial attention. Short forms of the 30-item Geriatric Depression Scale have been developed based on classical test theory, such as the GDS-4, GDS-5, a...