AIMC Topic: Humans

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Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring.

Biosensors
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selec...

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BMC medical imaging
BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission.

OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets.

BMC bioinformatics
BACKGROUND: Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typic...

An artificial intelligence-based risk prediction model of myocardial infarction.

BMC bioinformatics
BACKGROUND: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI ...

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.

Scientific reports
The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity ...

Machine Learning-Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance.

Journal of medical Internet research
BACKGROUND: Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clini...

Artificial Intelligence Algorithm in Classification and Recognition of Primary Hepatic Carcinoma Images under Magnetic Resonance Imaging.

Contrast media & molecular imaging
This study aimed to discuss the application value of the bias field correction algorithm in magnetic resonance imaging (MRI) images of patients with primary hepatic carcinoma (PHC). In total, 52 patients with PHC were selected as the experimental gro...

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

Computational intelligence and neuroscience
Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It...

Hybrid Approach Named HUAPO Technique to Guide the Lander Based on the Landing Trajectory Generation for Unmanned Lunar Mission.

Computational intelligence and neuroscience
This manuscript proposes a hybrid method for landing trajectory generation of unmanned lunar mission. The proposed hybrid control scheme is the joint execution of the human urbanization algorithm (HUA) and political optimizer (PO) with radial basis f...