AIMC Topic: Machine Learning

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Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study.

Hellenic journal of nuclear medicine
OBJECTIVE: The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emission tomography (F-FDG PET) at lateralizing seizure foci and diagnosing patients with unilateral temp...

Machine learning algorithm-based risk prediction model of coronary artery disease.

Molecular biology reports
In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic ri...

Chemical-induced disease relation extraction with dependency information and prior knowledge.

Journal of biomedical informatics
Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have ...

A machine learning method to monitor China's AIDS epidemics with data from Baidu trends.

PloS one
BACKGROUND: AIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Internet, the 'big data' aggregated from Internet search engines, which contain users' information on the ...

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.

Seizure
PURPOSE: Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. Using machine learning techniques we explored if abnormalities in microstates can identify patients ...

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Journal of applied clinical medical physics
BACKGROUND AND PURPOSE: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.

Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases.

International journal of environmental research and public health
Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and...

Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy.

Artificial intelligence in medicine
Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also st...

Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts.

Molecular pharmaceutics
Artificial Intelligence has advanced at an unprecedented pace, backing recent breakthroughs in natural language processing, speech recognition, and computer vision: domains where the data is euclidean in nature. More recently, considerable progress h...

Drug response prediction by ensemble learning and drug-induced gene expression signatures.

Genomics
Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in ad...