AIMC Topic: Bayes Theorem

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Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.

Sensors (Basel, Switzerland) Jan 19, 2023
Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to sele...
Computing Methodologies Humans Intensive Care Units Bayes Theorem Algorithms Artificial Intelligence
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A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease.

Sensors (Basel, Switzerland) Jan 18, 2023
According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. Howeve...
Hypothyroidism Support Vector Machine Calibration Bayes Theorem Humans Algorithms
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Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains.

International journal of molecular sciences Jan 16, 2023
Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 1023 and 1060. Th...
Bayes Theorem Drug Design Protein Domains Artificial Intelligence Molecular Docking Simulation Neural Networks, Computer
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Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions.

Journal of environmental management Jan 14, 2023
Nowadays, there is an increasing use of digital technologies and Artificial Intelligence (AI) such as Machine Learning (ML) models that leverage data to optimize the performances of systems in almost all activity sectors, including ML models for opti...
Carbon Footprint Carbon Dioxide Artificial Intelligence Bayes Theorem Neural Networks, Computer Humans
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Gradient Tree Boosting for Hierarchical Data.

Multivariate behavioral research Jan 5, 2023
Gradient tree boosting is a powerful machine learning technique that has shown good performance in predicting a variety of outcomes. However, when applied to hierarchical (e.g., longitudinal or clustered) data, the predictive performance of gradient ...
Bayes Theorem Linear Models Computer Simulation Algorithms Machine Learning
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Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation.

Journal of the Royal Society, Interface Jan 4, 2023
Inferring the underlying processes that drive collective behaviour in biological and social systems is a significant statistical and computational challenge. While simulation models have been successful in qualitatively capturing many of the phenomen...
Computer Simulation Bayes Theorem Algorithms Neural Networks, Computer Linear Models
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Exploratory pharmacovigilance with machine learning in big patient data: A focused scoping review.

Basic & clinical pharmacology & toxicology Jan 3, 2023
BACKGROUND: Machine learning can operationalize the rich and complex data in electronic patient records for exploratory pharmacovigilance endeavours.
Big Data Electronic Health Records Pharmacovigilance Humans Bayes Theorem Machine Learning
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An AI Approach for Identifying Patients With Cirrhosis.

Journal of clinical gastroenterology Jan 1, 2023
GOAL: The goal of this study was to evaluate an artificial intelligence approach, namely deep learning, on clinical text in electronic health records (EHRs) to identify patients with cirrhosis.
Neural Networks, Computer Artificial Intelligence Humans Liver Cirrhosis Bayes Theorem Algorithms Machine Learning
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Inferring turbulent environments via machine learning.

The European physical journal. E, Soft matter Dec 31, 2022
The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e.g., to precondition searching of optimal control policies in differen...
Bayes Theorem Neural Networks, Computer Machine Learning
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High throughput optimization of medium composition for Escherichia coli protein expression using deep learning and Bayesian optimization.

Journal of bioscience and bioengineering Dec 29, 2022
To improve synthetic media for protein expression in Escherichia coli, a strategy using deep neural networks (DNN) and Bayesian optimization was performed in this study. To obtain training data for a deep learning algorithm, E. coli harvesting a plas...
Escherichia coli Proteins Escherichia coli Green Fluorescent Proteins Bayes Theorem Deep Learning
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