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Introduction

Healthcare institutions are constantly looking for ways to improve patient outcomes and reduce costs. One area of interest is predicting patient readmission risk using electronic health records (EHR) and machine learning techniques. By accurately identifying patients at high risk of readmission, hospitals can intervene early and provide targeted care to improve patient outcomes and reduce healthcare costs.

This thesis aims to explore the use of machine learning algorithms on electronic health records to predict patient readmission risk. By analyzing a large dataset of patient health records, this study seeks to develop a predictive model that can accurately identify patients at high risk of readmission.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of patient readmission risk prediction
2.2 Electronic health records in healthcare
2.3 Machine learning algorithms in healthcare
2.4 Previous studies on predicting patient readmission risk
2.5 Factors contributing to patient readmissions
2.6 Challenges in predicting patient readmission risk
2.7 Best practices in managing patient readmission risk
2.8 Ethical considerations in using EHR for patient risk prediction
2.9 The role of healthcare providers in preventing readmissions
2.10 Future trends in predicting patient readmission risk

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection and preprocessing
3.3 Feature selection
3.4 Model development
3.5 Evaluation metrics
3.6 Validation techniques
3.7 Ethical considerations
3.8 Data analysis techniques

Chapter 4: Discussion of Findings
4.1 Analysis of predictive model performance
4.2 Identification of key predictors of readmission risk
4.3 Implications for healthcare providers
4.4 Comparison with existing readmission risk prediction models
4.5 Limitations of the study
4.6 Recommendations for future research
4.7 Practical implications for healthcare institutions
4.8 Policy implications
4.9 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for healthcare practice
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

Predicting patient readmission risk using electronic health records and machine learning is a critical area of research in healthcare. This thesis aims to develop a predictive model that can accurately identify patients at high risk of readmission by analyzing a large dataset of patient health records. The study will contribute to the growing body of literature on using machine learning algorithms to improve patient outcomes and reduce healthcare costs.

Chapter 1 provides an introduction to the research topic, highlighting the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review on patient readmission risk prediction, EHR usage in healthcare, machine learning algorithms, previous studies, factors contributing to readmissions, challenges, best practices, and ethical considerations.

Chapter 3 outlines the research methodology, including research design, data collection, preprocessing, feature selection, model development, evaluation metrics, validation techniques, ethical considerations, and data analysis techniques. Chapter 4 discusses the findings of the study, including an analysis of model performance, key predictors, implications for healthcare providers, comparisons with existing models, limitations, recommendations, practical and policy implications, and a conclusion.

Finally, Chapter 5 provides a summary of the findings, contributions to the field, implications for practice, recommendations for future research, and a conclusion. This thesis aims to advance our understanding of predicting patient readmission risk using EHR and machine learning techniques to improve patient outcomes and reduce healthcare costs.

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