AIMC Topic: Humans

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Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care.

International journal of medical informatics
BACKGROUND: Patient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not ca...

Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks.

Sensors (Basel, Switzerland)
Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose ...

Deep learning-based facial image analysis in medical research: a systematic review protocol.

BMJ open
INTRODUCTION: Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical cond...

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising acc...

A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma.

Physics in medicine and biology
Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate...

MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data.

PLoS computational biology
The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplas...

A Machine Learning Approach to Predictive Modelling of Student Performance.

F1000Research
- Many factors affect student performance such as the individual's background, habits, absenteeism and social activities. Using these factors, corrective actions can be determined to improve their performance. This study looks into the effects of th...

Tic Disorder of Children Analyzed and Diagnosed by Magnetic Resonance Imaging Features under Convolutional Neural Network.

Contrast media & molecular imaging
This work aimed to explore the analysis and diagnosis of children with tic disorder by magnetic resonance imaging (MRI) features under convolutional neural network (CNN), to provide a certain reference basis for clinical identification. A total of 45...

Multiscale Aggregate Networks with Dense Connections for Crowd Counting.

Computational intelligence and neuroscience
The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these probl...

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer.

Computational intelligence and neuroscience
Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for p...