Clinical Applications of Artificial Intelligence in Real-World Data

1. Front Matter

   Pages i-vi

   PDF

2. Data Processing, Storage, Regulations

   1. Front Matter

      Pages 1-1

      PDF

   2. Biomedical Big Data: Opportunities and Challenges (Overview)

      • Folkert W. Asselbergs, Spiros Denaxas, Jason H. Moore

      Pages 3-6

   3. Quality Control, Data Cleaning, Imputation

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

      Pages 7-36

   4. Data Standards and Terminology Including Biomedical Ontologies

      • Spiros Denaxas, Christian Stoeckert

      Pages 37-49

   5. Data Integration and Harmonisation

      • Maxim Moinat, Vaclav Papez, Spiros Denaxas

      Pages 51-67

   6. Natural Language Processing and Text Mining (Turning Unstructured Data into Structured)

      • Ayoub Bagheri, Anastasia Giachanou, Pablo Mosteiro, Suzan Verberne

      Pages 69-93

3. Analytics

   1. Front Matter

      Pages 95-95

      PDF

   2. Statistical Analysis—Measurement Error

      • Timo B. Brakenhoff, Maarten van Smeden, Daniel L. Oberski

      Pages 97-108

   3. Causal Inference and Non-randomized Experiments

      • Michail Katsoulis, Nandita Mitra, A. Floriaan Schmidt

      Pages 109-123

   4. Statistical Analysis—Meta-Analysis/Reproducibility

      • Mackenzie J. Edmondson, Chongliang Luo, Yong Chen

      Pages 125-139

   5. Machine Learning—Basic Unsupervised Methods (Cluster Analysis Methods, t-SNE)

      • M. Espadoto, S. B. Martins, W. Branderhorst, A. Telea

      Pages 141-159

   6. Machine Learning—Automated Machine Learning (AutoML) for Disease Prediction

      • Jason H. Moore, Pedro H. Ribeiro, Nicholas Matsumoto, Anil K. Saini

      Pages 161-173

   7. Machine Learning—Evaluation (Cross-validation, Metrics, Importance Scores...)

      • Abdulhakim Qahtan

      Pages 175-187

   8. Deep Learning—Prediction

      • Chris Al Gerges, Melle B. Vessies, Rutger R. van de Leur, René van Es

      Pages 189-202

   9. Deep Learning—Autoencoders

      • Melle Vessies, Rutger van de Leur, Philippe Wouters, René van Es

      Pages 203-220

   10. Artificial Intelligence

       • John H. Holmes

       Pages 221-230

   11. Machine Learning in Practice—Clinical Decision Support, Risk Prediction, Diagnosis

       • Amy Nelson, Parashkev Nachev

       Pages 231-245

   12. Machine Learning in Practice—Evaluation of Clinical Value, Guidelines

       • Luis Eduardo Juarez-Orozco, Bram Ruijsink, Ming Wai Yeung, Jan Walter Benjamins, Pim van der Harst

       Pages 247-261

   13. Challenges of Machine Learning and AI (What Is Next?), Responsible and Ethical AI

       • Polyxeni Gkontra, Gianluca Quaglio, Anna Tselioudis Garmendia, Karim Lekadir

       Pages 263-285

This book is a thorough and comprehensive guide to the use of modern data science within health care. Critical to this is the use of big data and its analytical potential to obtain clinical insight into issues that would otherwise have been missed and is central to the application of artificial intelligence. It therefore has numerous uses from diagnosis to treatment. 


Clinical Applications of Artificial Intelligence in Real-World Data is a critical resource for anyone interested in the use and application of data science within medicine, whether that be researchers in medical data science or clinicians looking for insight into the use of these techniques.

• Big health data
• Artificial intelligence
• Machine learning
• Deep learning
• Biomedical ontologies
• Electronic Health Records

• Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

  Folkert W. Asselbergs

• Institute of Health Informatics, University College London, London, UK

  Spiros Denaxas

• Department of Data Science and Biostatistics, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands

  Daniel L. Oberski

• Cedars-Sinai Medical Center, Los Angeles, USA

  Jason H. Moore

Dr Folkert Asselbergs is a clinical cardiologist at Amsterdam Heart Center, Prof of Precision medicine at the Institute of Health Informatics, University College London, director and founder of the BRC Clinical Research Informatics Unit and the recently initiated Nudging Unit at University College London Hospital, chair of the data infrastructure of the Dutch Cardiovascular Alliance, and associate editor of European Heart Journal for digital health and innovation. His research program focuses on translational data science using existing health data such as electronic health records and clinical registries enriched with novel modalities such as -omics and sensor data for knowledge discovery, drug target validation and precision medicine in cardiovascular disease.

 
Dr Spiros Denaxas is a Professor in Biomedical Informatics based at the Institute of Health Informatics at University College London and Associate Director leading phenomics at the British Heart Foundation Data Science Centre. His lab’s research focuses on creating and evaluating novel computational methods for data modelling, phenotyping, and disease subtype discovery in structured electronic health records.

Dr. Daniel L. Oberski is full professor of Health and Social Data Science with dual appointments at Utrecht University’s Department of Methodology & Statistics and the Department of Biostatistics and Data Science at the Julius Center, University Medical Center Utrecht (UMCU). His work focuses on applications of machine learning and data science to applied medical and social research, as well as the development of novel methods, often involving latent variable models. Among other roles, he is task coordinator of the Social Data Science team at the Dutch national infrastructure for the social sciences ODISSEI, and methodological lead at UMCU’s Digital Health team.



Dr. Jason Moore is founding Chair of the Department of Computational Medicine at Cedars-Sinai Medical Center where he also serves as founding Director of the Center for Artificial Intelligence Research and Education (CAIRE). He leads an active NIH-funded research program focused on the development and application of cutting-edge AI and machine learning algorithms for the analysis of biomedical data. His recent work has focused on methods for automated machine learning (AutoML) with a goal of democratizing AI in healthcare and biomedical research. He is an elected fellow of the American College of Medical Informatics, the International Academy of Health Sciences Informatics, the American Statistical Association, the International Statistics Institute, and the American Association for the Advancement of Science. He is Editor-in-Chief of the open-access journal BioData Mining.