AIMC Topic: Algorithms

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Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm.

American journal of surgery
BACKGROUND: Machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical m...

A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison.

Journal of medical systems
The study of electroencephalography (EEG) signals is not a new topic. However, the analysis of human emotions upon exposure to music considered as important direction. Although distributed in various academic databases, research on this concept is li...

Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms.

Sensors (Basel, Switzerland)
Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and ...

EL_LSTM: Prediction of DNA-Binding Residue from Protein Sequence by Combining Long Short-Term Memory and Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Most past works for DNA-binding residue prediction did not consider the relationships between residues. In this paper, we propose a novel approach for DNA-binding residue prediction, referred to as EL_LSTM, which includes two main components. The fir...

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach f...

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

IEEE transactions on medical imaging
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS)...

Learning to Segment Object Candidates via Recursive Neural Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effec...

Extracting psychiatric stressors for suicide from social media using deep learning.

BMC medical informatics and decision making
BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicid...

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

BMC medical informatics and decision making
BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and p...

Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge.

BMC medical informatics and decision making
BACKGROUND: The emergence of the deep convolutional neural network (CNN) greatly improves the quality of computer-aided supporting systems. However, due to the challenges of generating reliable and timely results, clinical adoption of computer-aided ...