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

Book Description

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 harmonization 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.

You’ll learn:

• 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

中文：

书名：实践医疗数据：驯服现实世界数据的复杂性

医疗保健是数据科学的下一个前沿。使用机器学习、深度学习和自然语言处理的最新技术，你将能够解决医疗保健中最紧迫的问题：降低医疗成本，确保患者得到最好的治疗，并在你学会如何访问和理解所有这些数据后，提高那些服务不足的人的可及性。

这本书提供了务实和实际的解决方案与医疗保健数据，从数据提取，清洁和协调，以功能工程。作者Andrew Nguyen介绍了特定的ML和深度学习示例，重点是生成高质量的数据。您将了解图形技术如何帮助您连接不同的数据源，以便您可以使用高级分析解决医疗保健领域最具挑战性的问题。

你将会学到：

• 不同类型的医疗数据：电子健康记录、临床登记和试验、数字健康工具和索赔数据
• 使用医疗数据的挑战，尤其是在尝试从多个来源聚合数据时
• 从临床文本中提取结构化数据的当前选项
• 如何在使用标准化结构化医疗数据的工具和框架时进行权衡
• 如何使用术语、本体以及映射和人行横道协调医疗数据