AIMC Topic: Bayes Theorem

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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...

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 ...

Machine-learning models for predicting surgical site infections using patient pre-operative risk and surgical procedure factors.

American journal of infection control
BACKGROUND: Surgical site infections (SSIs) are a significant health care problem as they can cause increased medical costs and increased morbidity and mortality. Assessing a patient's preoperative risk factors can improve risk stratification and hel...

Automatic Stones Classification through a CNN-Based Approach.

Sensors (Basel, Switzerland)
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of ""), financed by POR Calabria FE...

An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms.

Computational intelligence and neuroscience
In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid t...

PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data.

Journal of biomedical informatics
Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, howev...

The origin and evolution of open habitats in North America inferred by Bayesian deep learning models.

Nature communications
Some of the most extensive terrestrial biomes today consist of open vegetation, including temperate grasslands and tropical savannas. These biomes originated relatively recently in Earth's history, likely replacing forested habitats in the second hal...

A Bayesian deep learning method for freeway incident detection with uncertainty quantification.

Accident; analysis and prevention
Incident detection is fundamental for freeway management to reduce non-recurrent congestions and secondary incidents. Recently, machine learning technologies have made considerable progress in the incident detection field, but many still face challen...

Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production.

Chemosphere
The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk...

Machine learning analysis and prediction of N, NO, and O adsorption on activated carbon and carbon molecular sieve.

Environmental science and pollution research international
This research focuses on predicting the adsorbed amount of N, O, and NO on carbon molecular sieve and activated carbon using the artificial neural network (ANN) approach. Experimental isotherm data (data set 1242) on adsorbent type, gas type, tempera...