• Title: Fundamentals of Clinical Data Science
• Author(s) Pieter Kubben, Michel Dumontier, Andre Dekker
• Publisher: Springer; 1st ed. 2019 edition; eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Hardcover: 227 pages
• eBook: PDF and ePub
• Language: English
• ISBN-10/ASIN: 3319997122
• ISBN-13: 978-3319997124
• Share This:  

Book Description

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. An essential resource for healthcare professionals and IT consultants.

• Pieter Kubben is a neurosurgeon, mobile app developer and programme manager for eHealth and mHealth for the Maastricht University Medical Center.

• Amazon

• Data Science
• Data Analysis and Data Mining
• Big Data
• Natural Language Processing

• Fundamentals of Clinical Data Science (Pieter Kubben, et al.)
• The Mirror Site (1) - PDF

• Clinical Text Mining: Secondary Use of Electronic Patient Records

  This book describes the results of Natural Language Processing (NLP) and machine learning methods applied to clinical text from electronic patient records, provides a comprehensive overview of technical issues arising in clinical text mining.

• Theory and Applications for Advanced Text Mining (S. Sakurai)

  This book introduces advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. Text mining techniques have been studied aggressively in order to extract the knowledge from the data.

• Text Mining with R: A Tidy Approach (Julia Silge, et al)

  You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

• Data-Intensive Text Processing with MapReduce (Jimmy Lin)

  This free book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

• Text Processing in Python (David Mertz)

  This book is an example-driven, hands-on tutorial that carefully teaches programmers how to accomplish numerous text processing tasks using the Python language. It provides efficient and effective solutions to specific text processing problems.

• Mining Social Media: Finding Stories in Internet Data

  This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the Pandas library.

• Data Mining for the Masses (Matthew North)

  This book uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.

• Mining of Massive Datasets (Jure Leskovec, et al)

  It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.

• Text Algorithms (Maxime Crochemore, et al)

  This much-needed book on the design of algorithms and data structures for text processing emphasizes both theoretical foundations and practical applications. The core is the material on suffix trees and subword graphs, applications of these data structures.

• Metalearning: Applications to AutoML and Data Mining

  This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.