Data Mining in Biomedical Imaging, Signaling, and Systems (Hardcover)

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 presented. Given the widespread deployment of complex biomedical systems, the authors discuss system-engineering principles in a proposal for a design of reliable systems. This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems.

資料探勘可以幫助找出醫學資料中的隱藏資訊，並準確區分病理資料和正常資料。它可以幫助從患者群體和疾病狀態中提取隱藏的特徵，並在自動化決策中提供幫助。《生物醫學影像、信號和系統中的資料探勘》詳細探討了資料探勘在生物醫學和臨床應用中的應用。它提供了常見的異質資料模態的示例，並詳細介紹了用於解決分析複雜資料的計算挑戰的資料探勘方法的適用性。

該書詳細介紹了特徵提取技術，並涵蓋了幾個關鍵的特徵描述符。由於機器學習在許多診斷應用中被使用，因此它涵蓋了監督和非監督學習方法的基礎、評估指標和挑戰。特徵提取和監督學習都被討論，因為它們適用於癲癇患者的癲癇相關模式。還討論了其他特定疾病，以了解資料探勘在精煉臨床診斷中的價值，包括抑鬱症和反覆性偏頭痛。還描述了世界第四大嚴重健康威脅——抑鬱症的診斷和分級，以及可以區分抑鬱語音和正常語音的聲學特性的分析。儘管偏頭痛是一種複雜的神經系統疾病，但本書展示了代謝組學如何有效應用於臨床實踐。

作者們還回顧了基於對齊的聚類方法、自動分析生物膜圖像的技術，以及醫學文本探勘的應用，包括應用於醫學報告的文本分類。還討論了兩種危及生命的心臟異常——心律失常和缺血，並提出了一種用於挖掘三維影像生物標記的獨特分割方法，以評估骨關節炎為例。鑑於複雜生物醫學系統的廣泛應用，作者們在一個可靠系統設計的提案中討論了系統工程原則。這本綜合性的著作展示了資料探勘的廣泛應用範圍，並包括從生物醫學影像、信號和系統中分析資料的詳細策略和方法。