Hands-On Healthcare Data: Taming the Complexity of Real-World Data (Paperback)

Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved-once you learn how to access and make sense of all that data.

This book provides pragmatic and hands-on solutions for working with healthcare data, from data extraction to cleaning and normalizing to feature engineering. Author Andrew Nguyen covers specific ML and deep learning examples with a focus on producing high-quality data. You'll discover how graph technologies help you connect disparate data sources so you can solve healthcare's most challenging problems using advanced analytics.

With this book, you'll learn:

• The different types of healthcare data: electronic health records, clinical registries and trials, digital health tools, and claims data
• The challenges of working with healthcare data, especially when trying to aggregate data from multiple sources
• Current options for extracting structured data from clinical text
• How to make trade-offs when using tools and frameworks for normalizing structured healthcare data
• How to harmonize healthcare data using terminologies, ontologies, and mappings and crosswalks

醫療保健是數據科學的下一個領域。利用最新的機器學習、深度學習和自然語言處理技術，您將能夠解決醫療保健領域最迫切的問題：降低護理成本、確保患者獲得最佳治療，並提高弱勢群體的可及性-一旦您學會如何訪問和理解所有這些數據。

本書提供了實用且實用的解決方案，用於處理醫療保健數據，從數據提取到清理和規範化到特徵工程。作者Andrew Nguyen通過具體的機器學習和深度學習示例，重點介紹了如何生成高質量的數據。您將了解到圖形技術如何幫助您連接不同的數據源，以便使用高級分析解決醫療保健領域最具挑戰性的問題。

通過本書，您將學到：

- 不同類型的醫療保健數據：電子健康記錄、臨床註冊和試驗、數字健康工具和索賠數據
- 處理醫療保健數據時的挑戰，尤其是在嘗試從多個來源聚合數據時
- 從臨床文本中提取結構化數據的當前選項
- 在使用工具和框架規範化結構化醫療保健數據時如何進行權衡
- 如何使用術語、本體和映射以及交叉對應來協調醫療保健數據

Andrew Nguyen has been working at the intersection of healthcare data and machine learning for over a decade. He quickly discovered graph databases and has been using them to harmonize disparate data sources for nearly as long. Andrew holds a PhD in Biological and Medical Informatics from UCSF and a BS in Electrical and Computer Engineering from UCSD. He has worked for a variety of organizations, from academia to startups. He is currently a Principal Medical Informatics Architect at one of the largest biopharma companies in the world, where he is designing scalable solutions to harmonize healthcare real world data sources for machine learning and advanced analytics.

Andrew Nguyen在醫療數據和機器學習的交叉領域工作已有十多年的經驗。他很快發現了圖形數據庫，並且在近十年的時間裡一直在使用它們來協調不同的數據來源。Andrew擁有UCSF的生物醫學和醫學信息學博士學位，以及UCSD的電氣和計算機工程學士學位。他曾在從學術界到初創企業的各種組織工作過。他目前是世界上最大的生物製藥公司之一的首席醫學信息學架構師，他正在設計可擴展的解決方案，以協調醫療保健實際數據來源，用於機器學習和高級分析。