Intro
Foreword
Acknowledgments
Contents
Contributors
Part I: Fundamentals of Simulation
1: Simulation in Obstetric: From the History to the Modern Applications
1.1 Introduction
1.2 History of Obstetrical Simulation
1.3 The Twentieth Century Became a "Dark Age" for Simulation
1.4 The Role of Obstetrical Simulation Today
1.5 Future Perspectives
1.6 Conclusions
References
2: The Role of Simulation in Obstetric Schools in the UK
2.1 Introduction
2.2 The History of Obstetric Simulation Training

2.3 Simulation in UK Obstetrics and Gynaecology Training Programme
2.4 Simulation Training in Practice
2.5 Low-Fidelity Simulation
2.6 High-Fidelity Simulation
2.7 The Application of Simulation Training
2.8 Beyond the Technical Skills
2.9 Conclusion
References
3: Ontologies, Machine Learning and Deep Learning in Obstetrics
3.1 Integrated Care Pathways
3.1.1 Introduction
3.1.2 Artificial Intelligence and SaMD
3.1.2.1 Software as a Medical Device
3.1.2.2 Software as a Medical Device: Digital Therapies

3.1.2.3 Artificial Intelligence and Software as a Medical Devices
FDA Artificial Intelligence/Machine Learning Action Plan
The State of Artificial Intelligence-Based FDA-Approved Medical Devices and Algorithms: An Online Database
3.1.3 Pathology Innovation Collaborative Community (PICC)
3.1.4 Standard and Healthcare
3.1.4.1 The Clinical Element Model (CEM)
3.1.4.2 Electronic Medical Records (EMR)
3.1.4.3 Electronic Health Records (EHR)
3.1.4.4 openEHR
3.1.4.5 Health Level Seven (HL7)
3.1.4.6 Unified Medical Language System (UMLS)
3.1.4.7 CEN/ISO EN13606

3.1.5 Artificial Intelligence is the Way Forward in Obstetrics
3.2 Ontologies
3.2.1 Lists, Thesauri, and Taxonomies
3.2.2 How Ontologies Work
3.2.3 Particularities of Ontologies in the Medical Domain
3.2.4 Ontologies in Healthcare, Medical Data Collection Systems, and Their Use with Ontology-Based Symbolic AI Methods
3.2.5 Ontology Software Language, Ontology Editor, and Ontology Reasoner
3.2.6 New Frontiers for Ontology Reasoning from Symbolic AI to Non-symbolic AI
3.3 Machine Learning
3.3.1 Supervised Machine Learning Algorithms
3.3.1.1 Classification

Confusion Matrix
Accuracy
Precision
Recall or Sensitivity
Specificity
Class Imbalance Problem
Ensemble Techniques
3.3.1.2 Regression
3.3.1.3 Supervised Learning
Linear Regression and Logistic Regression (and Variants!)
Decision Tree and Random Forest Classifier
Naïve Bayes Classifier
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
3.3.2 Unsupervised Machine Learning Algorithms
3.3.2.1 Clustering
Measuring the Clustering Performance
Silhouette Analysis
Analysis of Silhouette Score
Calculating Silhouette Score