AIMC Topic: Bayes Theorem

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Efficient Graph Collaborative Filtering via Contrastive Learning.

Sensors (Basel, Switzerland)
Collaborative filtering (CF) aims to make recommendations for users by detecting user's preference from the historical user-item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-...

Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.

Frontiers in public health
South Africa (SA) has the highest incidence of colorectal cancer (CRC) in Sub-Saharan Africa (SSA). However, there is limited research on CRC recurrence and survival in SA. CRC recurrence and overall survival are highly variable across studies. Accu...

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome.

Journal of healthcare engineering
This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic ...

Multitask learning over shared subspaces.

PLoS computational biology
This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would b...

Urine cell image recognition using a deep-learning model for an automated slide evaluation system.

BJU international
OBJECTIVES: To develop a classification system for urine cytology with artificial intelligence (AI) using a convolutional neural network algorithm that classifies urine cell images as negative (benign) or positive (atypical or malignant).

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases.

Molecular diversity
With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, w...

Undersampling bankruptcy prediction: Taiwan bankruptcy data.

PloS one
Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losse...

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such...

Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.

Clinical neuroradiology
PURPOSE: The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The Multidimensional Prognostic Index (MPI) is an aggregate, comprehensive, geriatric assessment scoring system derived from eight domains that predict adverse outcomes, including 12-month mortality. However, the prediction accuracy of us...