AIMC Topic: Adult

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Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Current medical science
Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, especially in urban areas. Early prevention strategies are needed to reduce the associated mortality and morbidity. We applied the combination of rules and different mach...

Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Most brain lesions are characterized by hyperintense signal on FLAIR. We sought to develop an automated deep learning-based method for segmentation of abnormalities on FLAIR and volumetric quantification on clinical brain MRIs...

Comparisons among Machine Learning Models for the Prediction of Hypercholestrolemia Associated with Exposure to Lead, Mercury, and Cadmium.

International journal of environmental research and public health
Lead, mercury, and cadmium are common environmental pollutants in industrialized countries, but their combined impact on hypercholesterolemia (HC) is poorly understood. The aim of this study was to compare the performance of various machine learning ...

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques.

International journal of health geographics
BACKGROUND: Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjecti...

Assessing data availability and quality within an electronic health record system through external validation against an external clinical data source.

BMC medical informatics and decision making
BACKGROUND: Approximately 20% of deaths in the US each year are attributable to smoking, yet current practices in the recording of this health risk in electronic health records (EHRs) have not led to discernable changes in health outcomes. Several gr...

Response to repeat echoendoscopic celiac plexus neurolysis in pancreatic cancer patients: A machine learning approach.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
BACKGROUND: /Objectives: Efficacy of repeat echoendoscopic celiac plexus neurolysis is still unclear. Aim of the study was to assess the efficacy of repeat celiac plexus neurolysis and to build an artificial neural network model able to predict pain ...

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis.

Journal of dental research
This study's objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in te...

Use of Social Robots in Mental Health and Well-Being Research: Systematic Review.

Journal of medical Internet research
BACKGROUND: Technology-assisted clinical interventions are increasingly common in the health care field, often with the proposed aim to improve access to and cost-effectiveness of care. Current technology platforms delivering interventions are largel...

SpikeDeeptector: a deep-learning based method for detection of neural spiking activity.

Journal of neural engineering
OBJECTIVE: In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordin...

Biometric identification of listener identity from frequency following responses to speech.

Journal of neural engineering
OBJECTIVE: We investigate the biometric specificity of the frequency following response (FFR), an EEG marker of early auditory processing that reflects phase-locked activity from neural ensembles in the auditory cortex and subcortex (Chandrasekaran a...