AIMC Topic: Bayes Theorem

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GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients.

Nature communications
Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced survival. Liver biopsy, the diagnostic gold standard, is invasive and risky. We developed a hybrid multi-class neural network (NN) model, 'GraftIQ,' inte...

Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.

BMC infectious diseases
The increasing incidence of dengue virus (DENV) infections poses significant public health challenges in Bangladesh, demanding advanced forecasting methodologies to guide timely interventions. This study introduces a rigorous multivariate time series...

Clinical prediction of pathological complete response in breast cancer: a machine learning study.

BMC cancer
BACKGROUND: This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients.

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.

Nature communications
Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Existing approaches have been successful at explaining how humans generalize rapi...

MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning.

Scientific reports
This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress a...

Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users.

BMC medical research methodology
BACKGROUND: Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive p...

Machine learning approach for unmet medical needs among middle-aged adults in South Korea: a cross-sectional study.

BMC health services research
BACKGROUND: South Korea is reported to have higher levels of unmet medical needs (UMN) than other countries, particularly among the middle-aged adult population. Considering that this group constitutes a substantial portion of the country's productiv...

Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification.

Scientific reports
This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are cre...

Integrating Bayesian and neural networks models for eye movement prediction in hybrid search.

Scientific reports
Visual search is crucial in daily human interaction with the environment. Hybrid search extends this by requiring observers to find any item from a given set. Recently, a few models were proposed to simulate human eye movements in visual search tasks...

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.

Journal of chemical information and modeling
This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMI...