Practical guide to simulation in delivery room emergencies

Practical guide to simulation in delivery room emergencies / Gilda Cinnella, Renata Beck, Antonio Malvasi, editors.

In this book the use of hybrid simulation in delivery room emergencies is described and shown. The use of a patient actor combined with a task trainer within the same session substantially improve the training for practical management of intrapartum emergencies in real life, reducing the risk of fai...

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Bibliographic Details
Other Authors: Cinnella, Gilda (Editor), Beck, Renata (Editor), Malvasi, Antonio (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2023]
Subjects:
Emergency medical personnel > Training of.
Labor (Obstetrics) > Complications > Simulation games.
Emergency medical personnel > Training of
Online Access:Click for online access
Table of Contents:
  • 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

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