AIMC Topic: Humans

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VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

Computers in biology and medicine
INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing h...

Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models.

Sensors (Basel, Switzerland)
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usual...

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.

Sensors (Basel, Switzerland)
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows compr...

Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning methods for overall survival classification of Glioblastoma multiforme patients, and to robustify mod...

Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rare...

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except...

Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.

Physics in medicine and biology
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, m...

Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.

Journal of the Royal Society, Interface
We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep lea...

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.

PloS one
OBJECTIVES: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, ...