Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics
暫譯: 電子健康紀錄數據的統計與機器學習方法：從數據提取到數據分析

Wu, Hulin, Yamal, Jose Miguel, Yaseen, Ashraf

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

相關主題

商品描述

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. 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.

Key Features:

  • Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains.

  • Documents the detailed experience on EHR data extraction, cleaning and preparation

  • Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data.

  • Considers the complete cycle of EHR data analysis.

The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

商品描述(中文翻譯)

電子健康紀錄（EHR）/電子病歷（EMR）數據在研究中的使用越來越普遍。然而，這類數據的分析因其收集、處理方式及可回答的問題類型而面臨許多獨特的挑戰。本書涵蓋了許多與使用EHR/EMR數據進行研究相關的重要主題，包括數據提取、清理、處理、分析、推斷及基於多年實踐經驗的預測。書中仔細評估並比較了標準統計模型和方法與機器學習及深度學習方法，並報告了這些方法在基於EHR數據預測臨床結果時的無偏比較結果。

主要特點：
- 基於來自多學科EHR研究項目的貢獻者的實務經驗撰寫，涵蓋統計學、計算機科學、資訊學、數據科學及臨床/流行病學領域的方法和途徑。
- 記錄了EHR數據提取、清理和準備的詳細經驗。
- 提供了統計方法和機器學習預測模型的廣泛視角，以應對EHR數據的挑戰和限制。
- 考慮了EHR數據分析的完整循環。

EHR/EMR分析的使用需要統計學家、資訊學家、數據科學家及臨床/流行病學研究者之間的密切合作。本書反映了這種多學科的視角。

作者簡介

  • Hulin Wu, PhD, the endowed Betty Wheless Trotter Professor and Chair, Department of Biostatistics & Data Science, School of Public Health (SPH), University of Texas Health Science Center at Houston (UTHealth). Dr. Wu also holds a joined appointment as Professor at UTHealth School of Biomedical Informatics. Dr. Wu received BS and MS training in engineering and PhD in statistics. He has many years of experience in developing novel statistical methods, mathematical models and informatics tools for biomedical data analysis and modeling. He is the Founding Director of the Center for Big Data in Health Sciences (CBD-HS) and he is directing the EHR research working group at UTHealth SPH.

  • Dr. Yamal is a tenured Associate Professor in the Department of Biostatistics & Data Science and a member of the Coordinating Center for Clinical Trials at UTHealth　School of Public Health. Dr. Yamal has extensive experience in clinical trials including data coordinating centers and serving on Data Safety Monitoring Boards　for clinical　trials in stroke and traumatic brain injury. He has also contributed towards statistical methodology for　classification　problems for nested data as well as machine learning applications.　

  • Ashraf Yaseen is an Assistant Professor of Data Science at the School of Public Health, UTHealth. He has extensive experience in database design, implementation and management, machine learning, and high-performance computing. In his current research work, Dr. Yaseen is exploring big data integration and deep learning technologies in electronic health records to address clinical and public health questions.

  • Vahed Maroufy, PhD, Assistant Professor, Department of Biostatistics & Data Science, UTHealth School of Public Health. Dr. Maroufy received MSc and PhD training in statistics and has experience in applied and theoretical statistics, including geometry of statistical models, mixture models, Bayesian inference, predictive models using EHR data, and analysis of genetic data in cancer research.

作者簡介(中文翻譯)


  • 吳虎林
    • ，博士，德克薩斯大學健康科學中心休士頓分校公共衛生學院（UTHealth）生物統計與數據科學系的貴賓教授及系主任。吳博士同時擔任UTHealth生物醫學資訊學院的教授。吳博士在工程領域獲得學士及碩士學位，並在統計學獲得博士學位。他在開發新穎的統計方法、數學模型及生物醫學數據分析與建模的資訊工具方面擁有多年經驗。他是健康科學大數據中心（CBD-HS）的創始主任，並負責UTHealth公共衛生學院的電子健康紀錄（EHR）研究工作小組。



  • 雅馬爾博士是UTHealth公共衛生學院生物統計與數據科學系的終身副教授，並且是臨床試驗協調中心的成員。雅馬爾博士在臨床試驗方面擁有豐富的經驗，包括數據協調中心及擔任臨床試驗的數據安全監控委員會成員，涉及中風和創傷性腦損傷的臨床試驗。他還對於嵌套數據的分類問題及機器學習應用的統計方法學做出了貢獻。


  • 阿什拉夫·雅辛是UTHealth公共衛生學院的數據科學助理教授。他在數據庫設計、實施與管理、機器學習及高效能計算方面擁有豐富的經驗。在目前的研究工作中，雅辛博士正在探索電子健康紀錄中的大數據整合與深度學習技術，以解決臨床及公共衛生問題。




  • 瓦赫德·馬魯菲，博士，UTHealth公共衛生學院生物統計與數據科學系的助理教授。馬魯菲博士在統計學獲得碩士及博士學位，並在應用與理論統計方面擁有經驗，包括統計模型的幾何、混合模型、貝葉斯推斷、使用電子健康紀錄數據的預測模型，以及癌症研究中的遺傳數據分析。


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