AI Medical Compendium Topic

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Data Mining

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Automatically disambiguating medical acronyms with ontology-aware deep learning.

Nature communications
Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical no...

Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms.

Frontiers in immunology
RNA vaccines represent a milestone in the history of vaccinology. They provide several advantages over more traditional approaches to vaccine development, showing strong immunogenicity and an overall favorable safety profile. While preclinical testin...

Predicting affinity ties in a surname network.

PloS one
From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of i...

Difference Analysis of Regional Economic Development Based on the SOM Neural Network with the Hybrid Genetic Algorithm.

Computational intelligence and neuroscience
Since the reform and opening up, China's regional economy has developed rapidly. However, due to different starting points of economic development caused by the traditional distribution of productive forces and the differences in regions, resources, ...

English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining.

Computational intelligence and neuroscience
With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, becau...

Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and man...

Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool.

BMC medical research methodology
BACKGROUND: Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We evaluated a machine learning and text mining tool's ability to (a) automatically extract data elements from randomized t...

Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.

IEEE/ACM transactions on computational biology and bioinformatics
Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to...

Explaining Black-Box Models for Biomedical Text Classification.

IEEE journal of biomedical and health informatics
In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedical text classification. Using sources of domain knowledge and a confide...

The Infectious Disease Ontology in the age of COVID-19.

Journal of biomedical semantics
BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially f...