Natural Language Processing for Healthcare: The Rise of Intelligent Assistants
暫譯: 醫療保健的自然語言處理:智慧助手的崛起
Shaw, Laxmi, Mahajan, Shubham, Upreti, Kamal
- 出版商: Academic Press
- 出版日期: 2026-03-26
- 售價: $6,950
- 貴賓價: 9.5 折 $6,602
- 語言: 英文
- 頁數: 444
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0443452520
- ISBN-13: 9780443452529
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相關分類:
Natural Language Processing
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相關主題
商品描述
Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT. The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.
商品描述(中文翻譯)
《自然語言處理在醫療保健中的應用:智能助手的崛起》探討了尖端人工智慧研究與其在醫療保健實際應用之間的關鍵差距,提供了一本針對醫療環境獨特挑戰的易讀指南。書中強調自然語言處理(NLP)技術如何徹底改變病人護理、醫療文檔和臨床決策,同時強調倫理、法律和互操作性考量。全書分為四個部分,首先建立NLP和醫療數據的基礎知識,涵蓋如標記化(tokenization)、醫療本體(medical ontologies)如UMLS和SNOMED CT、機器學習模型,包括BioBERT和ClinicalBERT,以及大型語言模型如GPT的潛在影響。
應用部分探討了智能助手的實際應用案例,例如虛擬健康聊天機器人、臨床文檔工具、用於病人互動的對話式人工智慧,以及整合於電子健康紀錄中的語音識別。技術章節提供了系統架構、評估指標、數據隱私、安全性以及互操作性標準如FHIR的見解。最後一部分展望未來的方向,包括多語言NLP、用於隱私保護的聯邦學習,以及人工智慧驅動的醫療助手不斷演變的格局。本書是廣大讀者不可或缺的資源。