Information Discovery on Electronic Health Records (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
暫譯: 電子健康紀錄中的資訊發現(Chapman & Hall/CRC 數據挖掘與知識發現系列)
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- 出版商: CRC
- 出版日期: 2009-12-01
- 售價: $4,910
- 貴賓價: 9.5 折 $4,665
- 語言: 英文
- 頁數: 331
- 裝訂: Hardcover
- ISBN: 1420090380
- ISBN-13: 9781420090383
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相關分類:
Data-mining
海外代購書籍(需單獨結帳)
相關主題
商品描述
Exploiting the rich information found in electronic health records (EHRs) can facilitate better medical research and improve the quality of medical practice. Until now, a trivial amount of research has been published on the challenges of leveraging this information. Addressing these challenges, Information Discovery on Electronic Health Records explores the technology to unleash the data stored in EHRs.
Assembling a truly interdisciplinary team of experts, the book tackles medical privacy concerns, the lack of standardization for the representation of EHRs, missing or incorrect values, and the availability of multiple rich health ontologies. It looks at how to search the EHR collection given a user query and return relevant fragments from the EHRs. It also explains how to mine the EHR collection to extract interesting patterns, group entities to various classes, or decide whether an EHR satisfies a given property. Most of the book focuses on textual or numeric data of EHRs, where more searching and mining progress has occurred. A chapter on the processing of medical images is also included.
Maintaining a uniform style across chapters and minimizing technical jargon, this book presents the various ways to extract useful knowledge from EHRs. It skillfully discusses how EHR data can be effectively searched and mined.
商品描述(中文翻譯)
利用電子健康紀錄(EHRs)中豐富的信息可以促進更好的醫學研究並提高醫療實踐的質量。到目前為止,關於利用這些信息所面臨的挑戰的研究仍然非常有限。針對這些挑戰,電子健康紀錄的信息發現 探討了釋放存儲在EHR中的數據的技術。
本書組成了一支真正的跨學科專家團隊,處理醫療隱私問題、EHR表示的標準化缺乏、缺失或不正確的值,以及多種豐富健康本體的可用性。它探討了如何根據用戶查詢搜索EHR集合並返回相關的EHR片段。它還解釋了如何挖掘EHR集合以提取有趣的模式、將實體分組到各個類別,或決定某個EHR是否滿足給定的屬性。本書大部分內容集中在EHR的文本或數值數據上,這裡的搜索和挖掘進展較多。還包括一章關於醫學影像處理的內容。
本書在各章之間保持統一的風格,並最小化技術術語,展示了從EHR中提取有用知識的各種方法。它巧妙地討論了如何有效地搜索和挖掘EHR數據。