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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 4: Data Integration and Harmonisation
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Chapter title
Data Integration and Harmonisation
Chapter number 4
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_4
Book ISBNs
978-3-03-136677-2, 978-3-03-136678-9
Authors

Moinat, Maxim, Papez, Vaclav, Denaxas, Spiros

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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