Biomedical data mining

Date of publication:2008-2   Press: Shanghai science and technology   Author:Zhang Lu etc.   Pages:118   Words:145000  

At present, the human society is facing a residence exploding society. Information to reflect the state and characteristics of the three elements, and its material and energy together to form the society. However, residence is different from the feature of energy.

Data mining is an independent subject in the development of statistical analysis, pattern recognition, machine learning and database technology based on the. The new subject has a strong theoretical and practical application value, which is reflected in: combined with other disciplines including biomedical, provides new research methods for the development of these disciplines. This book will be the theory and practice of combining data mining, emphatically introduces the basic concept of data mining and its various practical application in the biomedical field, aims to familiarize the reader with the rational use of this method, to solve the basic medical research and clinical study of practical problems in. The first chapter introduces the basic concept of data mining; the second chapter introduces the method of data acquisition and data cleaning and data mining are required; eighth from the third chapter to chapter, combined with application examples at home and abroad, introduces regression analysis, association rule, time series analysis, sequence analysis, classification and clustering analysis of data mining methods, and gives the application examples of citation in the reference: the ninth chapter introduces the data mining software. The book is suitable for medical undergraduates and graduate students, basic medical research and clinical scientists, biomedical engineering students and technical personnel as the textbooks and reference materials used.
Catalogue of books

The first chapter is introduction 1.1 what is data mining 1.1.1 data, information and knowledge of 1.1.2 data mining definition 1.2 data mining application of 1.2.1 1.2.2 method 1.3 biomedical data mining the specificity of 1.3.1 medical data particularity 1.3.2 ethical, legal and social aspects of privacy sensitive issue 1.3.3 medical special properties of 1.4 data mining evaluation 1.4.1 samples of tissue 1.4.2 supervised learning evaluation 1.4.3 unsupervised learning evaluation of 1.5 data mining process for the second chapter of medical data acquisition and preparation for the 2.1 data collection and structure of 2.1.1 data collection, storage and management of 2.1.2 data organization 2.2 data preprocessing in 2.2.1 data preprocessing of 2.2.2 data distribution characteristics of 2.2.3 data cleaning 2.2.4 data integration of 2.2.5 data transform 2.2.6 data reduction third chapter 3.1 regression analysis regression analysis function of 3.2 commonly used regression analysis method of 3.2.1 linear regression 3.2.2 regression Logistic 3.2.3 3.2.4 artificial neural network and regression tree 3.3 regression Analysis and prediction of cervical cancer survival application a uterus. 3.3.1 research objectives analysis of 3.3.2 data acquisition and preprocessing of 3.3.3 data mining and analysis of 3.3.4 performance evaluation and comparison of the 3.4 regression analysis using a breast cancer prognosis analysis 3.4.1 research objective analysis of 3.4.2 data acquisition and preprocessing of 3.4.3 data mining and analysis of 3.4.4 performance evaluation and comparison of the fourth chapter of association rule 4.1 association rule features 4.1.1 association rules 4.1.2 association rules are defined and the importance of the 4.2 quality of the association rule analysis method 4.2.1 association rules analysis method 4.2.2 pruning and merging the 4.3 application of the association rules -- see the urine sickness patient screening 4.3.1 objective analysis of 4.3.2 data acquisition and pre processing of 4.3.3 data data mining and analysis 4.4 the application of association rules in hospital infection monitoring and control the 4.4.1 objective analysis of 4.4.2 data acquisition and preprocessing of 4.4.3 data mining and the fifth chapter is the analysis of time series analysis of 5.1 time series analysis function of 5.1.1 what is the time series data of 5.1.2 Sequence analysis of the function between the 5.2 methods of time series analysis of 5.2.1 time series data reduction and transformation of 5.2.2 time series data trend analysis of 5.2.3 time series data in the similarity of 5.3 with the application of time series analysis -- patients with type I diabetes blood sugar level changes of 5.3.1 research objective analysis of 5.3.2 data acquisition, processing and mining sixth chapter sequence analysis of 6.1 sequence analysis functions of 6.1.1 sequence data of the basic concepts of 6.1.2 sequence data analysis functions of 6.2 in biological sequence analysis method of 6.2.1 biomedical sequence data in the 6.2.2 biomedical data alignment of 6.3 sequences analysis of the application of a pregnancy drug side effect of 6.3.1 on the target analysis of 6.3.2 data acquisition and preprocessing in 6.3.3 data mining and analysis of the seventh chapter 7.1 classification functions of 7.1.1 classification and 7.1.2 classification method of 7.2 classification methods 7.2.1 classification method is the key technology of 7.2.2 feature selection 7.2.3 classifier selection 7.3 classification application of coronary heart disease Prediction of 7.3.1 target 7.3.2 data acquisition and processing of 7.3.3 data mining and analysis of 7.4 classification a classification of aphasia 7.4.1 application on 7.4.2 data acquisition and processing of 7.4.3 data mining and the eighth chapter is the analysis of clustering analysis of 8.1 cluster analysis function of 8.1.1 defined by the cluster analysis and 8.1.2 cluster analysis of the similarity measure of 8.2 cluster analysis method 8.2.1 8.2.2 cluster analysis method in the high dimensional feature space clustering 8.3 cluster analysis application -- inpatient population classification research objectives of 8.3.1 8.3.2 data acquisition and processing of 8.3.3 data mining and analysis of the ninth chapter 9.1 data mining software data mining software 9.1.1 data mining system product 9.1.2 how to select the data mining software 9.2 data mining software running environment 9.2.1 input data formatFunction of 9.2.2 data output form 9.3 data mining software structure of 9.3.1 SAS / Enterprise Miner 9.3.2 SPSS / Clementine function reference
Chapter excerpt

The first chapter is introduction 1.1 what is data mining 1.1.1 data, information and knowledge of data (data) is the characteristic state of the objective things record. For example, in some sales or sales, a class of drug use in hospital, a number of clinical departments, the bed turnover rate in patients with heart rate and blood pressure and other physiological parameters... These are the data. Objective things certain features of the state record also subject to technology. For example, in the previous X-ray and its application in medicine known, different attenuation characteristics of various tissue of human body cannot be recorded on x-ray. Therefore, with the development of human productivity and the progress of science and technology as well as human social activities, species, type and amount of data is increasing. At the same time, the development of computer and network as the representative of the information technology, the data acquisition, storage, management and reuse more convenient and specification (for example, through the database management system in a certain format or structure to store and organize the data), the data sharing increases (for example, sharing data via the Internet). In this context, "human beings are submerged in the" growing data is becoming one of the characteristics of the current society. On the other hand, the data is information (information) and knowledge (knowledge) carrier. Information and knowledge is really meaningful. However, compared to the rapid growth of data, the extraction of useful information from the data, and these information sum up for the ability of knowledge has greatly lagged behind (see figure L.1).
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