Prediction in Medicine: The Impact of Machine Learning on Healthcare

Table of Contents

CONTENTS

FOREWORD

PREFACE

LIST OF CONTRIBUTORS

CHAPTER 1 PREDICTIVE ANALYSIS: FORECASTING PATIENT'S OUTCOMES AND MEDICAL TRENDS

• Alka Singhal and Dhanalekshmi Gopinathan
• INTRODUCTION
• Impact of Technology on Healthcare
• Improved Patient Care
• Enhanced Diagnostics and Treatment
• Medication Management
• Preventive Healthcare
• Big Data and Analytics
• Improved Communication
• Enhanced Research and Development
• Patient Empowerment
• Efficiency and Cost Reduction
• Predictive Analysis and Healthcare
• Disease Prevention and Early Intervention
• Optimizing Treatment Plans
• Reducing Hospital Readmissions
• Resource Allocation and Operational Efficiency
• Chronic Disease Management
• Fraud Detection and Revenue Management
• Personalized Medicine
• Population Health Management
• Enhancing Patient Engagement
• Preparing for Public Health Challenges

PRINCIPLES OF HEALTH PREDICTIVE ANALYSIS

• Uncertainty and Error Measurement
• Focus of Health Forecasting
• Data Aggregation and Accuracy
• Horizons of Health Forecasting

PATTERNS IN HEALTH PREDICTIVE ANALYSIS

• Temporal Patterns
• Applications
• Example
• Spatial Patterns
• Applications
• Example
• Epidemiological Patterns
• Applications
• Example
• Genetic Patterns
• Applications
• Example
• Social and Behavioral Patterns
• Applications
• Example
• Clinical Patterns
• Applications
• Example
• Environmental Patterns
• Applications
• Example
• Pharmacological Patterns
• Applications
• Example
• Technological Patterns
• Applications
• Example
• Economic Patterns
• Applications
• Example

STEPS IN PREDICTIVE ANALYSIS MODELING

• Planning
• Problem Definition
• Data Collection
• Data Preparation
• Data Cleaning
• Feature Selection
• Model Building
• Algorithm Selection
• Training the Model
• Model Evaluation
• Validation Dataset
• Metrics
• Model Selection and Fine-Tuning
• Hyperparameter Tuning
• Comparing Models
• Implementation
• Deployment
• Monitoring and Maintenance
• Continuous Monitoring
• Model Maintenance
• Predictive Analytics Modeling

STEPS IN PREDICTIVE ANALYSIS MODELING IN HEALTHCARE

• Step 1
• Step 2
• Step 3
• Step 4
• Step 5
• Step 6
• Step 7
• Predictive Analysis in Healthcare Using Machine Learning
• Predictions on Cardiovascular Diseases
• Diabetes Predictions
• Hepatitis Disease Prediction
• Cancer Predictions Using Machine Learning
• Predictive Analysis in Healthcare Using Artificial Intelligence (AI)
• Disease Diagnosis and Risk Prediction
• Patient Outcomes and Treatment Optimization
• Chronic Disease Management
• Fraud Detection and Revenue Cycle Management
• Resource Allocation and Operational Efficiency
• Drug Discovery and Development
• Natural Language Processing (NLP) for Unstructured Data

CHALLENGES IN PREDICTIVE ANALYSIS IN HEALTHCARE

CONCLUSION

REFERENCES

CHAPTER 2 PREDICTION AND ANALYSIS OF DIGITAL HEALTH RECORDS, GEONOMICS, AND RADIOLOGY USING MACHINE LEARNING

• Sundeep Raj, Arun Prakash Agarwal, Sandesh Tripathi and Nidhi Gupta

INTRODUCTION

OVERVIEW OF ARTIFICIAL INTELLIGENCE

• Different Learning Methodologies
• Healthcare Applications of Artificial Intelligence
• Digital Health Records
• Radiology
• Genetic Engineering and Genomics

CHALLENGES AND RISKS

CONCLUSION

REFERENCES

CHAPTER 3 MEDICAL IMAGING USING MACHINE LEARNING AND DEEP LEARNING: A SURVEY

• Uma Sharma, Deeksha Sharma, Pooja Pathak, Sanjay Kumar Singh and Pushpanjali Singh

INTRODUCTION

MEDICAL IMAGE ANALYSIS

• Medical Imaging
• X-Ray Imaging
• Ultrasound Imaging
• Magnetic Resonance Imaging
• Computerized Tomography
• Mammography

MACHINE LEARNING

• Machine Learning Techniques
• Supervised Learning
• Unsupervised Learning

DEEP LEARNING

• CNN (Convolution Neural Network)
• Basic Building Blocks of CNN
• Convolutional Layer
• Rectified Linear Unit (RELU) or Activation Layer
• Pooling Layer
• Fully Connected Layer
• RNN (Recurrent Neural Network)
• MEDICAL IMAGING ANALYSIS WITH MACHINE LEARNING AND DEEP LEARNING
• Image Preprocessing
• Segmentation
• Feature Extraction
• Pattern Recognition or Classification

OPEN-SOURCE TOOLS

CONCLUSION

REFERENCES

CHAPTER 4 APPLICATIONS OF MACHINE LEARNING PRACTICES IN HUMAN HEALTHCARE MANAGEMENT SYSTEMS

• Ajay Satija, Priti Pahuja, Dipti Singh and Athar Hussain

INTRODUCTION

RESEARCH OBJECTIVES

NEED FOR MACHINE LEARNING IN THE HEALTHCARE INDUSTRY

CHALLENGES OF MACHINE LEARNING IN THE MEDICAL INDUSTRY

• Data Availability and Quality
• Data Security and Privacy
• Interpretability and Transparency
• Limited Sample Sizes
• Regulatory Compliance
• Integration into Healthcare Systems
• Bias and Fairness
• Clinical Adoption and Validation

APPLICATIONS OF MACHINE LEARNING IN HEALTHCARE

• Machine Learning in Medical Diagnosis
• Machine Learning in Clinical Trail
• Patient Enrolment and Eligibility Requirements
• Trial Protocol Design and Optimization
• Endpoint Prediction and Biomarker Identification
• Data Monitoring and Quality Assurance
• Drug Development and Discovery
• Predicting and Tracking Adverse Events
• Real-world Evidence (RWE) Generation
• Machine Learning in Drug Development
• Target Identification
• Predicting Drug-Drug Interactions
• Machine Learning Models Help with Drug Formulation Optimization
• Clinical Trial Optimization
• Drug Efficacy Prediction
• Drug Repurposing
• Toxicity Prediction
• Genomic Medicine
• Patient Stratification
• Utilization of Real-World Information
• Data Integration
• Market Access and Commercialization
• Robotic-based Surgery
• Machine Learning in Organ Image Processing

RISK MANAGEMENT IN HEALTHCARE THROUGH MACHINE LEARNING

• Finding and Preventing Fraud
• Medical Decision Assistance Frameworks

Author

• Neeta Verma
• Anjali Singhal
• Vijai Singh 
• Manoj Kumar