Brain-Body Interactions: Contemporary Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks and Fuzzy Logic

$69.00

Benjamin W. Y. Lo, MD, PhD
Departments of Neurosurgery, Neurology & Critical Care Medicine, Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada

Hitoshi Fukuda, MD, PhD
Department of Neurosurgery, Kurashiki Central Hospital, Kurashiki, Okayama, Japan

Yusuke Nishimura, MD, PhD
Department of Neurosurgery, Nagoya University, Nagoya, Japan

Yee-Yin Wan
Department of Business Administration, Hong Kong Shue Yan University, Hong Kong

Aurora W.M. Lo
Accountant, Private Practice, Japan

Series: Neuroscience Research Progress
BISAC: MED057000

This monograph serves as an in-depth guide to the use of the innovative combination of Bayesian analysis, artificial neural networks and fuzzy logic to create an individualized clinical prediction model applicable to many areas in medicine. This guide assumes no prior knowledge of advanced statistics or clinical medicine. Both the applied research scientist and clinician will be able to follow the clinical case of outcome prediction in ruptured brain aneurysms and apply this innovative prognostication model to different areas in medicine.

By using Bayesian neural networks with fuzzy logic inferences, the practitioner can create a system that incorporates one’s own experience (Bayesian concepts), recognizes unknown areas in medicine (artificial neural networks) and grey zones in diagnoses and prognoses (fuzzy logic inferences). This monograph also profiles contemporary research advances in the diagnosis and treatment of aneurysmal subarachnoid hemorrhage. Application of this clinical prediction modelling system in the case of ruptured brain aneurysms has led to clarification of clinical prognostication in this area, as well as discovery of brain-body interactions that are important in influencing outcome in these patients. The potential impact of such a monograph is to demonstrate how to create such clinical outcome prediction models, as well to help find new prognostic factors and brain-body interactions, that when recognized and treated early, can lead to better clinical outcomes for the patient. (Imprint: Nova Biomedical )

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Table of Contents

Table of Contents

Preface

About the Authors

Chapter 1 – Introduction (pp. 1-8)

Chapter 2 – Fuzzy Logic Approach to Bayesian Neural Networks: A Conceptual Framework (pp. 9-16)

Chapter 3 – Fuzzy Logic Approach to Bayesian Neural Networks: A Theoretical Framework (pp. 17-32)

Chapter 4 – Introduction to Aneurysmal Subarachnoid Hemorrhage and Brain-Body Interactions (pp. 33-42)

Chapter 5 – Brain-Body Interactions and Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Multivariable Binary Logistic Regression (pp. 43-56)

Chapter 6 – Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Logistic Regression (pp. 57-74)

Chapter 7 – Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Artificial Neural Networks (pp. 75-88)

Chapter 8 – Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Fuzzy Logic (pp. 89-100)

Chapter 9 – Brain-Body Interactions: Contemporary Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks and Fuzzy Logic (pp. 101-106)

Index

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