Biomedical Data Mining, Spatial

2017 ◽  
pp. 127-135
Author(s):  
Keith Marsolo ◽  
Michael Twa ◽  
Mark A. Bullimore ◽  
Srinivasan Parthasarathy
Keyword(s):  
Data Mining ◽  
Biomedical Data

Biomedical data mining for improved clinical diagnosis

2021 ◽  
pp. 155-176
Author(s):  
G. Nalinipriya ◽  
M. Geetha ◽  
R. Cristin ◽  
Balajee Maram
Keyword(s):  
Data Mining ◽  
Clinical Diagnosis ◽  
Biomedical Data

A Data Mining Based Approach for Collaborative Analysis of Biomedical Data

2014 ◽  
Vol 23 (04) ◽  
pp. 1460010 ◽  
Author(s):  
Georgia Tsiliki ◽  
Sophia Kossida ◽  
Natalja Friesen ◽  
Stefan Rüping ◽  
Manolis Tzagarakis ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Biomedical Data ◽  
Data Repositories ◽  
Mining Algorithm ◽  
Research Questions ◽  
Collaborative Analysis ◽  
Set Up ◽  
Available Information ◽  
Collaborative Decision

Biomedical research becomes increasingly multidisciplinary and collaborative in nature. At the same time, it has recently seen a vast growth in publicly and instantly available information. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data mining algorithm to genomic data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and interpreting biomedical data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data. Evaluation results show that the proposed approach facilitates users to set their research objectives and better understand the data and methodologies used in their research.

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 70-71
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Quality Control ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Systematic Approach ◽  
Biomedical Data ◽  
Control Methods ◽  
Data Quality Control ◽  
Health Records

Chen J, Podchiyska T, Altman R. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records. J Am Med Inform Assoc 2016;23:339-48 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009921/ Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci Rep 2016;6:26094 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4869115/ Prasser F, Kohlmayer F, Kuhn KA. The Importance of Context: Risk-based De-identification of Biomedical Data. Methods Inf Med 2016;55:347-55 https://methods.schattauer.de/en/contents/archivestandard/issue/2382/manuscript/25994.ht Saez C, Zurriaga O, Perez-Panades J, Melchor I, Robles M, Garcia-Gomez JM. Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. J Am Med Inform Assoc 2016;23:1085-95 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw010

SmartPortal for Biomedical Data Mining

2007 ◽  
Author(s):  
Anna L. Buczak ◽  
Charles Wan ◽  
Glenn Petry
Keyword(s):  
Data Mining ◽  
Biomedical Data

scholarly journals Bayesian rule learning for biomedical data mining

2010 ◽  
Vol 26 (5) ◽  
pp. 668-675 ◽  
Author(s):  
Vanathi Gopalakrishnan ◽  
Jonathan L. Lustgarten ◽  
Shyam Visweswaran ◽  
Gregory F. Cooper
Keyword(s):  
Data Mining ◽  
Rule Learning ◽  
Biomedical Data

scholarly journals Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

2017 ◽  
Vol 21 ◽  
Author(s):  
Trung Duy Pham ◽  
Dat Tran ◽  
Wanli Ma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Digital Data ◽  
Series Data ◽  
Biomedical Data ◽  
Protection Mechanism ◽  
Wide Range ◽  
Watermarking Scheme ◽  
Ownership Protection

In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a right-protection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and re-sampling.

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