Data mining in biomedical imaging, signaling, and systems
著者
書誌事項
Data mining in biomedical imaging, signaling, and systems
CRC Press, c2011
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
Data mining can help pinpoint hidden information in medical data and accurately differentiate pathological from normal data. It can help to extract hidden features from patient groups and disease states and can aid in automated decision making. Data Mining in Biomedical Imaging, Signaling, and Systems provides an in-depth examination of the biomedical and clinical applications of data mining. It supplies examples of frequently encountered heterogeneous data modalities and details the applicability of data mining approaches used to address the computational challenges in analyzing complex data.
The book details feature extraction techniques and covers several critical feature descriptors. As machine learning is employed in many diagnostic applications, it covers the fundamentals, evaluation measures, and challenges of supervised and unsupervised learning methods. Both feature extraction and supervised learning are discussed as they apply to seizure-related patterns in epilepsy patients. Other specific disorders are also examined with regard to the value of data mining for refining clinical diagnoses, including depression and recurring migraines. The diagnosis and grading of the world's fourth most serious health threat, depression, and analysis of acoustic properties that can distinguish depressed speech from normal are also described. Although a migraine is a complex neurological disorder, the text demonstrates how metabonomics can be effectively applied to clinical practice.
The authors review alignment-based clustering approaches, techniques for automatic analysis of biofilm images, and applications of medical text mining, including text classification applied to medical reports. The identification and classification of two life-threatening heart abnormalities, arrhythmia and ischemia, are addressed, and a unique segmentation method for mining a 3-D imaging biomarker, exemplified by evaluation of osteoarthritis, is also present
目次
Data Mining of Acoustical Properties of Speech as Indicators of Depression. Artificial Neural Network Based ECG Arrhythmia Classification. Human Reflexive Response and its Objective Function Regarding Balance Recovery From Perturbation During Walking. Automatic Identification of Epileptic EEG Signals Using Nonlinear Parameters. Data Mining Approach to Classify the Pathological Images in Databases Using Color Image Analysis. Discovery of Association Among Diseases in the Upper Gastro Intestinal Tract Using Data Mining Techniques. Data Analysis Techniques Applied to Metabolomics: Analysis and Classification of Migraine Patterns. System Engineering Principles in the Design of Biomedical Systems. Feature Classification Methods for Knowledge Discovery in Mammograms. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteosrthritis. Typicality Measure and the Creation of Predictive Models in Biomedicine. Automatic Segmentation Methods and Applications to Biofilm Image Analysis. On Clustering Gene Expression Time-Series Signals. Multi-Scale Method For Biomedical Image Segmentation.
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