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Clinical Applications of Artificial Intelligence in Real-World Data

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Cover of 'Clinical Applications of Artificial Intelligence in Real-World Data'

Table of Contents

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    Book Overview
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    Chapter 1 Biomedical Big Data: Opportunities and Challenges (Overview)
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    Chapter 2 Quality Control, Data Cleaning, Imputation
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    Chapter 3 Data Standards and Terminology Including Biomedical Ontologies
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    Chapter 4 Data Integration and Harmonisation
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    Chapter 5 Natural Language Processing and Text Mining (Turning Unstructured Data into Structured)
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    Chapter 6 Statistical Analysis—Measurement Error
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    Chapter 7 Causal Inference and Non-randomized Experiments
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    Chapter 8 Statistical Analysis—Meta-Analysis/Reproducibility
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    Chapter 9 Machine Learning—Basic Unsupervised Methods (Cluster Analysis Methods, t-SNE)
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    Chapter 10 Machine Learning—Automated Machine Learning (AutoML) for Disease Prediction
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    Chapter 11 Machine Learning—Evaluation (Cross-validation, Metrics, Importance Scores...)
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    Chapter 12 Deep Learning—Prediction
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    Chapter 13 Deep Learning—Autoencoders
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    Chapter 14 Artificial Intelligence
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    Chapter 15 Machine Learning in Practice—Clinical Decision Support, Risk Prediction, Diagnosis
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    Chapter 16 Machine Learning in Practice—Evaluation of Clinical Value, Guidelines
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    Chapter 17 Challenges of Machine Learning and AI (What Is Next?), Responsible and Ethical AI
Attention for Chapter 2: Quality Control, Data Cleaning, Imputation
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Summary
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Chapter title
Quality Control, Data Cleaning, Imputation
Chapter number 2
Book title
Clinical Applications of Artificial Intelligence in Real-World Data
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-36678-9_2
Book ISBNs
978-3-03-136677-2, 978-3-03-136678-9
Authors

Liu, Dawei, Oberman, Hanne I., Muñoz, Johanna, Hoogland, Jeroen, Debray, Thomas P. A.

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 20%
Student > Bachelor 1 20%
Student > Master 1 20%
Unknown 2 40%
Readers by discipline Count As %
Engineering 2 40%
Psychology 1 20%
Unknown 2 40%

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