Data Mining and Predictive Modeling of Biomolecular Network from Biomedical Literature Databases

2007 ◽  
Vol 4 (2) ◽  
pp. 251-263 ◽  
Author(s):  
Xiaohua Hu ◽  
Daniel D. Wu
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Biomedical Literature ◽  
Biomolecular Network

Behavioral Targeting Online Advertising

2011 ◽  
pp. 213-232 ◽  
Author(s):  
Jun Yan ◽  
Dou Shen ◽  
Teresa Mah ◽  
Ning Liu ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Predictive Modeling ◽  
Rapid Growth ◽  
Online Advertising ◽  
Research Challenges ◽  
Behavioral Targeting ◽  
The Us ◽  
Collection Data ◽  
Complex Technology

With the rapid growth of the online advertising market, Behavioral Targeting (BT), which delivers advertisements to users based on understanding of their needs through their behaviors, is attracting more attention. The amount of spend on behaviorally targeted ad spending in the US is projected to reach $4.4 billion in 2012 (Hallerman, 2008). BT is a complex technology, which involves data collection, data mining, audience segmentation, contextual page analysis, predictive modeling and so on. This chapter gives an overview of Behavioral Targeting by introducing the Behavioral Targeting business, followed by classic BT research challenges and solution proposals. We will also point out BT research challenges which are currently under-explored in both industry and academia.

Overview of Predictive Modeling Approaches in Health Care Data Mining

2016 ◽  
pp. 73-95 ◽  
Author(s):  
Sunita Soni
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Clinical Information ◽  
Ease Of Use ◽  
Classification Model ◽  
Computational Time ◽  
Data Sets ◽  
Advantages And Disadvantages ◽  
Mining Methods ◽  
Predictive Data Mining

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

Aggregation for Predictive Modeling with Relational Data

2011 ◽  
pp. 33-38
Author(s):  
Claudia Perlich ◽  
Foster Provost
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Relational Databases ◽  
Feature Vector ◽  
Relational Data ◽  
Government Organizations ◽  
Modeling Techniques ◽  
Aggregation Of Information ◽  
New Challenges

Most data mining and modeling techniques have been developed for data represented as a single table, where every row is a feature vector that captures the characteristics of an observation. However, data in most domains are not of this form and consist of multiple tables with several types of entities. Such relational data are ubiquitous; both because of the large number of multi-table relational databases kept by businesses and government organizations, and because of the natural, linked nature of people, organizations, computers, and etc. Relational data pose new challenges for modeling and data mining, including the exploration of related entities and the aggregation of information from multi-sets (“bags”) of related entities.

Predictive modeling of hospital readmissions using metaheuristics and data mining

2015 ◽  
Vol 42 (20) ◽  
pp. 7110-7120 ◽  
Author(s):  
Bichen Zheng ◽  
Jinghe Zhang ◽  
Sang Won Yoon ◽  
Sarah S. Lam ◽  
Mohammad Khasawneh ◽  
...  
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Hospital Readmissions

scholarly journals Decision Tree: A Predictive Modeling Tool Used in Cloud Trust Prediction

2019 ◽  
Vol 8 (6) ◽  
pp. 4779-4783
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Modeling ◽  
Cloud Provider ◽  
Data Mining Algorithm ◽  
Trust Value ◽  
The Public ◽  
Mining Algorithm ◽  
Current Scenario ◽  
Reliable Basis

Trust is one of the important challenges faced by the cloud industry. Ever increasing data theft cases are contributing in worsening this issue. Regarding trust, author has a perception that this challenge can be handled to some extend if consumer can evaluate “Trust Value “ of the provider or can predict the same on some reliable basis. Current research is using predictive modeling for predicting trustworthiness of cloud provider. This paper is an attempt to utilize the data mining algorithm for predictive modeling. Decision Tree, a supervised data mining algorithm has been used in the current work for making predictions. Certification attainment criteria as prime basis for trust evaluation. In current scenario, data mining algorithm will classify providers in category of low, medium and high category of trust on the basis of information displayed on the public domain

scholarly journals A Data Mining-Based Analysis of Medication Rules in Treating Bone Marrow Suppression by Kidney-Tonifying Method

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xing You ◽  
Yinkun Xu ◽  
Jin Huang ◽  
Yaofeng Zhi ◽  
Fengzhen Wu ◽  
...  
Keyword(s):  
Data Mining ◽  
Bone Marrow ◽  
Chinese Medicine ◽  
High Frequency ◽  
Bone Marrow Suppression ◽  
Biomedical Literature ◽  
Astragali Radix ◽  
Knowledge Infrastructure ◽  
Pathogenic Factors ◽  
Chinese National Knowledge Infrastructure

Objective. To investigate the rule of kidney-tonifying method in Chinese medicine for the treatment of bone marrow suppression (BMS), in order to provide evidence and references for the clinical application of herbs and formulae. Design. Collecting and sorting the information about the treatment of BMS related to kidney-tonifying (Bushen) method in Chinese medicine literatures on databases including Chinese National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBM), establishing a database of BMS treating formulae after radiotherapy and chemotherapy with traditional Chinese medicine (TCM) kidney-tonifying method, and finally applying the relevant theories and techniques of data mining to analyze the medication rules of it. Results. A total of 239 formulae and 202 herbs were included in this database, in which the herbs occurred 2,602 times in general. The high frequency herbs included Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizoma (Baizhu), and Ligustri Lucidi Fructus (Nvzhenzi). The main herb categories were deficiency-tonifying herbs, blood-activating herbs, dampness-draining diuretic herbs, heat-clearing herbs, and digestant herbs. Deficiency-tonifying herbs accounted for 64.60% of the total number. A total of 8 clustering formulae are summarized according to cluster analysis and 26 herb suits association rules are identified by Apriori algorithm. Conclusion. The treatment of BMS is mainly based on the method of invigorating the spleen and tonifying the kidney and liver to strengthen healthy qi, supplementing with blood-activating herbs, and dampness-draining diuretic herbs to eliminate pathogenic factors.

Data Mining Medical Digital Libraries

2011 ◽  
pp. 334-340
Author(s):  
Colleen Cunningham
Keyword(s):  
Data Mining ◽  
Growth Rate ◽  
Text Mining ◽  
Digital Libraries ◽  
Exponential Growth ◽  
Medical Data ◽  
Biomedical Literature ◽  
Exponential Growth Rate ◽  
Biological Functions ◽  
Data Mining Techniques

Given the exponential growth rate of medical data and the accompanying biomedical literature, more than 10,000 documents per week (Leroy et al., 2003), it has become increasingly necessary to apply data mining techniques to medical digital libraries in order to assess a more complete view of genes, their biological functions and diseases. Data mining techniques, as applied to digital libraries, are also known as text mining.

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