AI Medical Compendium Topic

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Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning.

Sensors (Basel, Switzerland)
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prio...

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...

Machine-learning model selection and parameter estimation from kinetic data of complex first-order reaction systems.

PloS one
Dealing with a system of first-order reactions is a recurrent issue in chemometrics, especially in the analysis of data obtained by spectroscopic methods applied on complex biological systems. We argue that global multiexponential fitting, the still ...

A process mining approach in big data analysis and modeling decision making risks for measuring environmental health in institutions.

Environmental research
This paper aimed to introduce a process-mining framework for measuring the status of environmental health in institutions. The methodology developed a new software-based index namely Institutional Environmental Health Index (IEHI) that was integrated...

Exploring polypharmacy with artificial intelligence: data analysis protocol.

BMC medical informatics and decision making
BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and s...

A deep look into radiomics.

La Radiologia medica
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into ...

Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study.

The Lancet. Digital health
BACKGROUND: Early detection of postoperative complications, including organ failure, is pivotal in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an era of increasing health-care costs and limited financial reso...

Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.

PloS one
The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, a...

Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.

BMC bioinformatics
BACKGROUND: Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and...

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.

Molecular diversity
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing...