AIMC Topic: Algorithms

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Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning.

European radiology
OBJECTIVE: The aim of this study was to investigate whether intraplacental texture features from routine placental MRI can objectively and accurately predict invasive placentation.

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Epilepsia
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluation...

Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.

BMC medical informatics and decision making
BACKGROUND: A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the ...

Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

IEEE transactions on visualization and computer graphics
This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D m...

Peak alignment of gas chromatography-mass spectrometry data with deep learning.

Journal of chromatography. A
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for bioma...

Prediction of Potential Drug-Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features.

International journal of molecular sciences
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and disea...

A Cancer Survival Prediction Method Based on Graph Convolutional Network.

IEEE transactions on nanobioscience
BACKGROUND AND OBJECTIVE: Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant...

CCMapper: An adaptive NLP-based free-text chief complaint mapping algorithm.

Computers in biology and medicine
OBJECTIVE: Chief complaint (CC) is among the earliest health information recorded at the beginning of a patient's visit to an emergency department (ED). We propose a heuristic methodology for automatically mapping the free-text data into a structured...

A critique of pure learning and what artificial neural networks can learn from animal brains.

Nature communications
Artificial neural networks (ANNs) have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals (including humans), training such networks requires enormous numbers of labeled examples, l...