This reference demonstrates the development of a context aware decision-making health informatics system with the objective to automate the analysis of human centric wellness and assist medical decision-making in healthcare.
The book introduces readers to the basics of a clinical decision support system. This is followed by chapters that explain how to analyze healthcare data for anomaly detection and clinical correlations. The next two sections cover machine learning techniques for object detection and a case study for hemorrhage detection. These sections aim to expand the understanding of simple and advanced neural networks in health informatics. The authors also explore how machine learning model choices based on context can assist medical professionals in different scenarios.
Key Features
- Reader-friendly format with clear headings, introductions and summaries in each chapter
- Detailed references for readers who want to conduct further research
- Expert contributors providing authoritative knowledge on machine learning techniques and human-centric wellness
- Practical applications of data science in healthcare designed to solve problems and enhance patient wellbeing
- Deep learning use cases for different medical conditions including hemorrhages, gallbladder stones and diabetic retinopathy
- Demonstrations of fast and efficient CNN models with varying parameters such as Single shot detector, R-CNN, Mask R-CNN, modified contrast enhancement and improved LSTM models.
This reference is intended as a primary resource for professionals, researchers, software developers and technicians working in healthcare informatics systems and medical diagnostics. It also serves as a supplementary resource for learners in bioinformatics, biomedical engineering and medical informatics programs and anyone who requires technical knowledge about algorithms in medical decision support systems.
Readership
Healthcare professionals, software developers, engineers, diagnostic technicians, students, academicians and machine learning enthusiasts.
Table of Contents
FOREWORD
PREFACE
CHAPTER 1 INTRODUCTION
1. INTRODUCTION
2. NEED FOR CLINICAL DECISION SUPPORT SYSTEM
3. FEATURE SELECTION AND DATA ANALYSIS IN THE HEALTHCARE FIELD
3.1. Disease Diagnosis and Prognosis
3.2. Personalized Medicine
3.3. Medical Imaging
3.4. Operational Efficiency
3.5. Electronic Health Records (EHR) Management
4. EFFECTIVENESS OF FEATURE SELECTION AND DATA ANALYSIS IN THE HEALTH CARE FIELD
4.1. Improved Predictive Accuracy
4.2. Reduction of Overfitting
4.3. Cost Reduction
4.4. Enhanced Interpretability
5. CUTTING-EDGE TECHNOLOGIES IN HEALTHCARE DOMAIN
5.1. Artificial Intelligence (AI) in Healthcare
5.2. CRISPR and Gene Editing
5.3. Nanomedicine and Microrobots
5.4. 3D Bioprinting and Smart Implants
5.5. Social Determinants of Health (SDOH)
5.6. Wearable Technology and IoMT
5.7. Advanced Imaging Techniques
5.8. Value-Based Care
6. SCOPE OF THE BOOK
7. MOTIVATION
8. PROBLEM STATEMENT
9. OBJECTIVES AND CHALLENGES
10. BOOK CONTRIBUTIONS
11. ORGANIZATION OF THE BOOK
REFERENCES
CHAPTER 2 ANALYZING HEALTHCARE DATA TO IDENTIFY ANOMALIES AND CORRELATIONS
1. INTRODUCTION
2. LITERATURE REVIEW
3. DATA DESCRIPTION AND ANALYSIS
4. SYSTEM DESCRIPTION
4.1. Data Collection
4.2. Data Pre-processing
4.2.1. Handling Missing Values
4.3. Data Integration and Transformation
4.3.1. Aggregation
4.3.2. Normalization
4.3.3. Smoothing
4.4. Classification Algorithms
4.4.1. K - Nearest Neighbor Algorithm
5. RESULT ANALYSIS
6. SUMMARY
REFERENCES
CHAPTER 3 OBJECT DETECTION FOR HEALTHCARE DATA USING DEEP CONVOLUTIONAL NEURAL NETWORKS
1. INTRODUCTION
2. LITERATURE REVIEW
3. OBJECT DETECTION ALGORITHMS
3.1. One-Stage Object Detection Algorithms
3.1.1. Single Shot Detectors
3.2. Multi-Stage Object Detection Algorithms
3.2.1. Faster R - CNN
3.2.2. Mask R - CNN
3.3. Mask Representation
3.4. RoIAlign
4. OBJECT DETECTION DATASETS
5. SYSTEM DESCRIPTION
6. RESULT ANALYSIS
7. SUMMARY
REFERENCES
CHAPTER 4 AN ENHANCED DEEP LEARNING TECHNIQUE TO DETECT AND CLASSIFY HEMORRHAGES BASED ON CNN WITH IMPROVED LSTM BY HYBRID METAHEURISTIC ALGORITHM
1. INTRODUCTION
1.1. Diabetes
1.2. Diabetic Retinopathy
2. LITERATURE REVIEW
2.1. Understanding and Treatment of Diabetic Retinopathy
2.2. Diabetic Retinopathy Symptoms
2.3. Excudate DR
3. PROPOSED METHODOLOGY
3.1. Background of Long Short-Term Memory (LSTM)
3.2. DCNN
3.3. Harris Hawks Optimization (HHO)
3.4. Pre-Processing Using (CLAHE)
3.5. Feature Extraction
3.5.1. Input layer
3.5.2. Convolutional Layer (CL)
3.5.3. Pooling Layer (PL)
3.5.4. Fully Connected Layers (FCLs)
3.6. Classification
3.7. Optimized Long Short-Term Memory
4. EXPERIMENTAL RESULTS AND DISCUSSION
4.1. Dataset Description
4.2. Performance Analysis of the Proposed Model
4.3. An In-Depth Analysis Contrasting the Suggested Methods with Those Already in Use 115
5. SUMMARY
REFERENCES
CHAPTER 5 CONCLUSION
1. CONCLUDING REMARKS
2. FUTURE WORK AND CONSTRAINTS OF THE ALGORITHM
SUBJECT INDEX
Author
- Veena A
- Gowrishankar S
