Maintaining imbalance highly dependent medical data using dirichlet process data generation

2011 ◽  
Author(s):  
Tieta Antaresti ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Dirichlet Process ◽  
Medical Data ◽  
Data Generation ◽  
Process Data

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

Mechanistic Modeling and Simulation for Process Data Generation

2019 ◽  
Vol 58 (38) ◽  
pp. 17871-17884
Author(s):  
Natércia C. P. Fernandes ◽  
Andrey Romanenko ◽  
Marco S. Reis
Keyword(s):  
Modeling And Simulation ◽  
Mechanistic Modeling ◽  
Data Generation ◽  
Process Data

Software Design and Implementation of 3D Multi-GNSS Visualization System

2013 ◽  
Vol 765-767 ◽  
pp. 936-940
Author(s):  
Xi Cai ◽  
Cheng Dong Xu ◽  
Chun Sheng Hu
Keyword(s):  
3D Visualization ◽  
3D Models ◽  
Main Process ◽  
Generation Process ◽  
Data Generation ◽  
Process Data ◽  
Display Module ◽  
Visualization System ◽  
Modeling Software ◽  
General Architecture

In order to provide technical supports and visualization tools to the multi-GNSS related researches, 3D multi-GNSS visualization system (3DMVS) is designed and implemented. In this paper, the system requirements are analyzed at first and four modules (user interface module; display module; date calculating module; 3D models library module) are designed to realize the 3D visualization of the earth, satellites and orbits in different navigation system. Then the system general architecture is presented. Next, the system main process, data generation process and data visualization process is introduced. Dataflow between modules are illustrated in details by four steps. The visual C# programming tools and the 3D modeling software ZAM3D are used in the course of system implementation. The system has successfully applied in the related researches of multi-GNSS.

scholarly journals C3IMD : An Efficient Class-Based Clustering Classifier for Im-putation Intelligent Medical Data

2018 ◽  
Vol 7 (2.27) ◽  
pp. 255
Author(s):  
P. Premalatha ◽  
S Subasree ◽  
N K Sakthivel
Keyword(s):  
Classification Accuracy ◽  
Missing Values ◽  
Research Work ◽  
Medical Application ◽  
Medical Data ◽  
Data Sets ◽  
Data Generation ◽  
Huge Amount ◽  
Sensitivity Specificity ◽  
Fast Evolution

The fast evolution in medical application yields to abundance of huge amount of data in volume and velocity.  Due to this heterogeneous medical data generation from clinical trials, its typically not free from missing values.  Previously introduced imputation techniques don’t discourse the high spatiality problems and application of distance function that even have curse on high spatiality problem. Thus, there’s a necessity an Efficient and Accurate technique to overcome this problem in Medical Data Analysis. To address the above mentioned issues, this research work proposed an efficient Class-Based Clustering Classifier for Imputation Intelligent Medical Data (C3IMD).  This work was implemented in Bio Weka and studied thoroughly. To improve the classification and prediction accuracy, missing data in Medical Data Sets were filled efficiently with the help of proposed Cluster-Classifier Model. The experiments are repeated with various datasets and results are evaluated and compared with existing classifiers WPT-DELM and SVM-DELM. From the results obtained, it was revealed that the proposed Class-Based Clustering Classifier for Imputation Intelligent Medical Data (C3IMD) is outperforming both the existing models in terms of Classification Accuracy, Sensitivity, Specificity and FScore.  

scholarly journals Feasibility Queries in Distributed Architectures – Concept and Implementation in HiGHmed

2021 ◽  
Author(s):  
Reto Wettstein ◽  
Hauke Hund ◽  
Insa Kobylinski ◽  
Christian Fegeler ◽  
Oliver Heinze
Keyword(s):  
Research Question ◽  
Routine Data ◽  
Medical Data ◽  
Third Party ◽  
University Hospitals ◽  
Process Data ◽  
Hl7 Fhir ◽  
Open Standard ◽  
Set Up ◽  
Institutional Boundaries

Medical routine data promises to add value for research. However, the transfer of this data into a research context is difficult. Therefore, Medical Data Integration Centers are being set up to merge data from primary information systems in a central repository. But, data from one organization is rarely sufficient to answer a research question. The data must be merged beyond institutional boundaries. In order to use this data in a specific research project, a researcher must have the possibility to query available cohort sizes across institutions. A possible solution for this requirement is presented in this paper, using a process for fully automated and distributed feasibility queries (i.e. cohort size estimations). This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4 resources. The proposed solution is currently being deployed at eight university hospitals and one trusted third party across Germany.

A Nonparametric Bayesian Approach to Multiple Instance Learning

2015 ◽  
Vol 29 (03) ◽  
pp. 1551001
Author(s):  
Achut Manandhar ◽  
Kenneth D. Morton ◽  
Leslie M. Collins ◽  
Peter A. Torrione
Keyword(s):  
Bayesian Approach ◽  
Dirichlet Process ◽  
Cross Validation ◽  
Multiple Instance Learning ◽  
Data Generation ◽  
Dirichlet Process Mixture ◽  
Nonparametric Bayesian ◽  
Variational Bayesian Inference ◽  
Proper Number ◽  
Proposed Model

Multiple instance learning (MIL) is a type of supervised learning in which labels are available for sets of observations (bags), but not for individual observations (instances). MIL has been applied in different areas, which has led to a large number of algorithms for learning based on MIL data. Many of these approaches focus on maximizing class margins, performing instance selection, or developing distance metrics and kernels suitable for application directly to bags. Although these approaches have shown promise, most require cross-validation-based optimization of hyper parameters or iterative numerical optimization to determine the proper number of target concepts. This work proposes a nonparametric Bayesian approach to learning in MIL scenarios based on Dirichlet process mixture models. The nonparametric nature of the model and the use of noninformative priors remove the need to perform cross-validation-based optimization while variational Bayesian inference allows for rapid parameter learning. The resulting approach generalizes to different applications by easily incorporating alternate data generation models. In a related effort [A. Manandhar et al., IEEE Trans. Geosci. Remote Sensing53(4) (2015) 1737–1745.], the proposed model has been extended to incorporate time-varying data. Results indicate that when the data generation assumption holds, the proposed approach performs competitively with existing MIL and nonMIL methods for several standard MIL datasets and a new MIL dataset introduced in this work.

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