AIMC Topic: Machine Learning

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Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

Sensors (Basel, Switzerland)
The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable perform...

Exploring Antiviral Drugs on Monolayer Black Phosphorene: Atomistic Theory and Explainable Machine Learning-Assisted Platform.

International journal of molecular sciences
Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density funct...

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Journal of translational medicine
BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related ...

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study.

BMC cancer
OBJECTIVE: The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).

Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners.

Scientific reports
Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, t...

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

Scientific reports
COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effec...

Prediction and causal inference of hyperuricemia using gut microbiota.

Scientific reports
Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease....

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

eLife
Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective vi...

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis.

Neurosurgical review
BACKGROUND: Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, m...

Machine learning in nanozymes: from design to application.

Biomaterials science
Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable act...