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on1382526152 |
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20240909213021.0 |
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230616s2023 sz a o 000 0 eng d |
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|a YDX
|b eng
|e rda
|e pn
|c YDX
|d GW5XE
|d EBLCP
|d OCLCF
|d OCLCQ
|d OCLCO
|d N$T
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| 019 |
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|a 1382694049
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|a 9783031100673
|q (electronic bk.)
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|a 3031100670
|q (electronic bk.)
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|z 9783031100666
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|z 3031100662
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7 |
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|a 10.1007/978-3-031-10067-3
|2 doi
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| 035 |
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|a (OCoLC)1382526152
|z (OCoLC)1382694049
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|a RG703
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| 049 |
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|a HCDD
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| 245 |
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|a Practical guide to simulation in delivery room emergencies /
|c Gilda Cinnella, Renata Beck, Antonio Malvasi, editors.
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|a Cham :
|b Springer,
|c [2023]
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| 264 |
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|c ©2023
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| 300 |
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|a 1 online resource (xxvii, 1036 pages) :
|b illustrations (chiefly color)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 520 |
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|a 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 failure of operative vaginal delivery and of related adverse events, including perinatal or maternal complications. Furthermore, simulation with high reality computerized mannequin and scenography of emergency situation can improve technical and manual skills of the participants. For this book and the related videos, a new generation of mannequins suitable for both clinical manoeuvres and ultrasound examination is used to simulate all clinical scenarios of emergency that can happen in the delivery room for both the mother and the child. This unique book is a useful tool for medical students, residents, practicing pediatricians, anesthetists, obstetricians and all health care professionals working in the delivery room in their ability to deal with critical and emergency situations with safety and good medical practice.
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|a Online resource; title from PDF title page (SpringerLink, viewed June 26, 2023).
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| 505 |
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|a 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
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| 505 |
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|a 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
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|a 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
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| 505 |
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|a 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
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| 505 |
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|a 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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| 650 |
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|a Emergency medical personnel
|x Training of.
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| 650 |
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|a Labor (Obstetrics)
|x Complications
|x Simulation games.
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| 650 |
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|a Emergency medical personnel
|x Training of
|2 fast
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| 700 |
1 |
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|a Cinnella, Gilda,
|e editor.
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| 700 |
1 |
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|a Beck, Renata,
|e editor.
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| 700 |
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|a Malvasi, Antonio,
|e editor.
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| 776 |
0 |
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|i Print version:
|z 3031100662
|z 9783031100666
|w (OCoLC)1322812160
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-10067-3
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
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