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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Mã sản phẩm:
Danh mục: Sách ngoại ngữ, Sách IT, Sách chuyên ngành, Sách Tiếng Anh quyển Lẻ
Tình trạng: Còn hàng

Mô tả

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

Sách Hands-On Healthcare Data Taming the Complexity of Real-World Data (sách keo gáy, bìa mềm)

Categories:Computers - Organization and Data Processing

Year:2022

Edition:1

Language:english

Pages:245

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. But first, you have to 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

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