AIMC Topic: Machine Learning

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A machine learning method to estimate PM concentrations across China with remote sensing, meteorological and land use information.

The Science of the total environment
BACKGROUND: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at...

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques.

Journal of Korean medical science
BACKGROUND: Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previou...

Automated screening of research studies for systematic reviews using study characteristics.

Systematic reviews
BACKGROUND: Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train ...

Assessing Breast Cancer Risk with an Artificial Neural Network.

Asian Pacific journal of cancer prevention : APJCP
Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk. Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer. This study aimed to esta...

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

Bone
INTRODUCTION: The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis.

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.

Neural networks : the official journal of the International Neural Network Society
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve ab...

Multi-target drug repositioning by bipartite block-wise sparse multi-task learning.

BMC systems biology
BACKGROUND: Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 database provide gene expression profiles induced by...

Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

BMC bioinformatics
BACKGROUND: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to...

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned pare...

An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF.

IEEE/ACM transactions on computational biology and bioinformatics
To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this ...