AIMC Topic: Machine Learning

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Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.

Journal of imaging informatics in medicine
This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retr...

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study.

Urology
OBJECTIVE: To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.

Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.

The Science of the total environment
Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture t...

COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset.

Artificial intelligence in medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affecte...

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan.

Ecotoxicology and environmental safety
Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used m...

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

ACS sensors
Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the ...

Investigation of bias in the automated assessment of school violence.

Journal of biomedical informatics
OBJECTIVES: Natural language processing and machine learning have the potential to lead to biased predictions. We designed a novel Automated RIsk Assessment (ARIA) machine learning algorithm that assesses risk of violence and aggression in adolescent...

Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping.

ACS nano
Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coup...

Advances in Machine Learning for Wearable Sensors.

ACS nano
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare....

Kinematics-Based Predictions of External Loads during Handcycling.

Sensors (Basel, Switzerland)
The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be bene...