AIMC Topic: Humans

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Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort.

BMC medicine
BACKGROUND: The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among prete...

Perspectives on usability and accessibility of an autonomous humanoid robot living with elderly people.

Disability and rehabilitation. Assistive technology
PURPOSE: The aim of this study was to assess how cohabitation with a social robot for seven days night and day is perceived by elderly potential users and to suggest technological improvements for future home integration.

Feasibility of a 5G-Based Robot-Assisted Remote Ultrasound System for Cardiopulmonary Assessment of Patients With Coronavirus Disease 2019.

Chest
BACKGROUND: Traditional methods for cardiopulmonary assessment of patients with coronavirus disease 2019 (COVID-19) pose risks to both patients and examiners. This necessitates a remote examination of such patients without sacrificing information qua...

Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will signif...

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity.

Computer methods in biomechanics and biomedical engineering
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem....

Identification of Patients with Heart Failure in Large Datasets.

Heart failure clinics
Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of case...

Radiomics in radiation oncology-basics, methods, and limitations.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machin...

Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Nature reviews. Gastroenterology & hepatology
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome anal...

Comparing the capabilities of transfer learning models to detect skin lesion in humans.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This arti...

Bayer's in silico ADMET platform: a journey of machine learning over the past two decades.

Drug discovery today
Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early dru...