AIMC Topic: Machine Learning

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Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis.

Journal of cardiac surgery
BACKGROUND: Machine learning (ML) models are promising tools for predicting adverse postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use. We conducted a systematic review and meta-analysis to assess the predictiv...

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus.

Current opinion in rheumatology
PURPOSE OF REVIEW: Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study.

Artificial intelligence in medicine
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical ...

Machine learning method for extracting elastic modulus of cells.

Biomechanics and modeling in mechanobiology
The Hertz contact mechanics model is commonly used to extract the elastic modulus of the cell, but the basic assumptions of the model are often not met in cell indentation experiments, which can lead to errors in the obtained elastic modulus of cell....

Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models.

Sensors (Basel, Switzerland)
Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with...

A Probability-Based Models Ranking Approach: An Alternative Method of Machine-Learning Model Performance Assessment.

Sensors (Basel, Switzerland)
Performance measures are crucial in selecting the best machine learning model for a given problem. Estimating classical model performance measures by subsampling methods like bagging or cross-validation has several weaknesses. The most important ones...

An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition.

Computational intelligence and neuroscience
Smart grid is regarded as an evolutionary regime of existing power grids. It integrates artificial intelligence and communication technologies to fundamentally improve the efficiency and reliability of power systems. One serious challenge for the sma...

Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan.

PloS one
The aging world population requires a sustainable and high-quality healthcare system. To examine the efficiency of medical cooperation, medical provider and physician networks were constructed using patient claims data. Previous studies have shown th...

Classification of Musculoskeletal Radiograph Requisition Appropriateness Using Machine Learning.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Poor quality imaging requisitions lower report quality and impede good patient care. Manual control of such requisitions is time consuming and can be a source of friction with referring physicians. The purpose of this study was to determine if poor ...