Statistics and Machine Learning Methods for Ehr Data: From Data Extraction to Data Analytics

ISBN-13: 9780367442392 / Angielski / Twarda / 2020 / 313 str.

Hulin Wu; Jose Miguel Yamal; Ashraf Yaseen

Opis

The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data.

Spis treści

Preface

About the Editors

List of Contributors

1. Introduction: Use of EHR Data for Scientific Discoveries—Challenges and Opportunities

Hulin Wu

2. EHR Project Management

Yashar Talebi and Ashraf Yaseen

3. EHR Databases and Data Management: Data Query and Extraction

Gen Zhu, Vi K. Ly, Michael Gonzalez, Leqing Wu, Hulin Wu, and Ashraf Yaseen

4. EHR Data Cleaning

Yashar Talebi, Han Feng, Yuefan Huang, and Vahed Maroufy

5. EHR Data Pre-Processing and Preparation

Duo Yu, Xueying Wang, and Hulin Wu

6. EHR Missing Data Issues

Chenguang Zhang, Vahed Maroufy, Baojiang Chen, and Hulin Wu

7. Causal Inference and Analysis for EHR Data

Stacia DeSantis, Momiao Xiong, Jose-Miguel Yamal, Gen Zhu, Duo Yu, Xueying Wang, Chenguang Zhang, and Vi K. Ly

8. EHR Data Exploration, Analysis and Predictions: Statistical Models and Methods

Gen Zhu, Frances Brito, Stacia M DeSantis, and Vahed Maroufy

9. Neural Network and Deep Learning Methods for EHR Data

Duo Yu, Ashraf Yaseen, and Xi Luo

10. EHR Data Analytics and Predictions: Machine Learning Methods

Yuxuan Gu, Yuefan Huang, Vi Ly, Ashraf Yaseen, and Hongyu Miao

11. Use of EHR Data for Research: Future

Hulin Wu

Index