AIMC Topic: Machine Learning

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Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.

Advanced materials (Deerfield Beach, Fla.)
Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and h...

Data mining and machine learning in HIV infection risk research: An overview and recommendations.

Artificial intelligence in medicine
In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the stud...

Are the criteria for PD-MCI diagnosis comprehensive? A Machine Learning study with modified criteria.

Parkinsonism & related disorders
BACKGROUND: Mild cognitive impairment in Parkinson's disease (PD-MCI) includes deficits in different cognitive domains, and one domain to explore for neurocognitive impairment following the DSM-V is social cognition. However, this domain is not inclu...

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

The journal of obstetrics and gynaecology research
AIM: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurren...

Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.

Journal of neuro-oncology
OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differe...

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.

Journal of chemical information and modeling
Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current ...

Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.

Environmental science and pollution research international
Carbon dioxide (CO) emissions result from human activities like burning fossil fuels. CO is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO emissions include transitioning to renewable energy. Monitoring and ...

Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model.

Environmental science and pollution research international
Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic ...

Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.

The Journal of forensic odonto-stomatology
BACKGROUND: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, superv...