Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases.
Contents
1 INTRODUCTION
1.1 Organisation of Thesis
1.2 Motivation and aims
1.3 Original Contributions
1.4 Publications
2 LITERATURE REVIEW
2.1 Clinical Decision Support Systems
2.1.1 Ontology Driven Clinical Decision Support Frameworks
2.1.2 Clinical Decision Support Systems in Cardiovascular Care
2.1.3 Cardiovascular Risk Estimation Systems for Disease Prevention
2.1.4 Machine Learning Driven Cardiovascular Decision Support Systems
2.1.5 Role of Feature Selection in Clinical Decision Support Systems
2.2 Conclusion and Discussion
3 A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care
3.1 Proposed Framework
3.2 ODCRARS for Cardiovascular Preventative Care
3.2.1 Ontology driven intelligent context aware information collection component
3.2.2 Patient Medical Records
3.2.3 Ontology Driven Decision Support
3.3 Machine Learning Driven Prognostic Modelling for Cardiovascular Preventative Care
3.4 Machine Learning Driven Prognostic Model
3.4.1 Data Acquisition
3.4.2 Data Pre-Processing
3.4.3 Feature Selection
3.4.4 Prognostic Model Development
3.4.5 Prognostic Model Validation and Evaluation
3.4.6 Online Clinical Prognostic Model
3.5 Conclusion and Discussion
4 Ontology Driven Clinical Risk Assessment and Recommendation System (ODCRARS) for Cardiovascular Preventative Care
4.1 Implementation of the Ontology Driven Clinical Risk Assessment and Recommendation System (ODCRARS)
4.2 Ontology driven intelligent context aware information collection: Design and Implementation
4.2.1 Ontology Driven Intelligent Context Aware Ontology Model
4.2.2 Adaptive Clinical Questionnaire: Design and Implementation
4.2.3 Proposed Novel Decision Tree based Approach
4.2.4 Dynamic Adaptation
4.3 Patient Medical Records
4.4 Patient Semantic Profile : Design and Implementation
4.4.1 Ontology Development
4.5 Ontology Driven Clinical Decision Support: Design and Implementation
4.5.1 Recommendation Ontology
4.6 Clinical Rules Engine: Design and Implementation
4.6.1 Clinical Rules Data - Patient Fact Representation
4.6.2 Jess: Java based Rules Engine
4.6.3 Partitioning the Rules
4.6.4 Cardiovascular Risk Assessment
4.7 System Implementation: Integration of ODCRARS and MLDPS
4.7.1 Patient Module
4.8 Doctor’s Module
4.8.1 Integration of the ODCRARS with the machine learning driven cardiac chest pain and heart disease prognostic models
4.9 Conclusion and Discussion
5 Machine Learning Driven Prognostic System (MLDPS) for Cardiovascular Preventative Care
5.1 Case Study 1: Rapid Access Chest Pain Clinic
5.1.1 Background
5.1.2 Aims
5.2 RACPC Clinical Dataset 1
5.2.1 Data Acquisition
5.2.2 Data Preparation
5.2.3 Missing Data Handling
5.2.4 Feature Selection
5.2.5 Prognostic Model Development: Experimental Setups and Results
5.2.6 Final Diagnosis
5.2.7 Evaluation of RACPC Results
5.2.8 Results of Comparative Machine Learning Classification
5.2.9 Analysis of Variance (ANOVA) Test for Performance Evaluation
5.3 RACPC Clinical Dataset 2: Demonstrating Effects of missing Data on Verification Results
5.3.1 Background
5.3.2 Pre-processing of Missing Data using Probability Estimation
5.3.3 Expectation Maximisation (EM) Approach
5.3.4 Experiments
5.3.5 Classification for the Incomplete Clinical Data
5.3.6 Filling the Incomplete Data
5.4 RACPC Clinical Case Study: RACPC Clinical Dataset 3
5.4.1 Study Group 1: Clinical Risk Factors
5.4.2 Evaluation
5.4.3 Performance evaluation of experimental setups
5.4.4 Study Group 2: Test Results
5.4.5 Evaluation
5.4.6 Performance evaluation of experimental setups
5.4.7 Implementation of online Clinical Prognostic Models
5.4.8 Machine Learning Driven Cardiac chest pain prognostic model’s integration with the recommendation system
5.5 Case Study 2: Heart Disease
5.5.1 Background
5.5.2 Aims
5.5.3 Data Preparation
5.5.4 Feature Selection
5.5.5 Prognostic Model Development
5.5.6 Prognostic Model Validation and Evaluation
5.5.7 Performance evaluation of experimental setups
5.5.8 Implementation of online Clinical Prognostic Models
5.6 Case Study 3: Breast Cancer Prognostic Modelling
5.6.1 Background
5.6.2 Aims
5.6.3 Candidate Clinical Variable Selection
5.6.4 Prognostic Model Development
5.6.5 Prognostic Model Validation and Evaluation
5.6.6 Performance Evaluation of Experimental Setups
5.6.7 Online Clinical Prognostic Model
5.7 Verification and Validation of the Clinical Prototypes
5.7.1 Validation of the Machine Learning Driven System (MLDPS) and Ontology Driven Clinical Risk Assessment and Recommendation System (ODCRARS)
5.8 Summary and Conclusion
6 CONCLUSIONS AND FUTURE WORK
6.1 Conclusions
6.2 Discussion and Summary of Contributions
6.3 Future Work
6.3.1 Utilisation of Fuzzy Cognitive Maps for Collaborative Care
6.3.2 Active Manifold Learning Strategy in Machine Learning Driven Prognsotic Modelling based on Big Data
6.4 Limitations
Objectives and Topics
This thesis aims to develop a novel hybrid clinical decision support framework for cardiovascular preventative care by integrating ontology-driven risk assessment with machine learning-based prognostic modeling. The primary research question addresses how to effectively leverage legacy clinical patient data and standardized clinical guidelines to improve patient-centric, evidence-based care in primary and secondary healthcare settings.
- Development of an Ontology-Driven Clinical Risk Assessment and Recommendation System (ODCRARS) for patient interviews and clinical rules implementation.
- Creation of a Machine Learning Driven Prognostic System (MLDPS) for predicting cardiovascular risks and other chronic conditions like breast cancer.
- Implementation of context-aware, adaptive information collection techniques using decision tree-based questionnaires.
- Evaluation of various machine learning and feature selection techniques for prognostic model performance in clinical datasets.
Book Excerpt
Ontology Driven Intelligent Context Aware Information Collection Component
Healthcare information systems are widely used all over the world to alleviate diverse healthcare demands and supply gaps [82]. Clinical systems based on information collection through questionnaires are fundamental to the core functioning of healthcare information management systems. With the recent success of electronic healthcare records globally, information collection through intelligent means has now become one of the most important components of modern healthcare systems. In modern patient interviewing/screening systems, one of the main challenges to date is to get patients involved in the clinical decision making process by getting them to interact with usable information collection systems to collect their medical records.
Healthcare resources in most parts of the world are stretched to the limit which is why healthcare providers’ main focus is to build preventative care solutions based on patient medical records. Patient triage systems are more in demand than ever before, demonstrating why they are an essential component of healthcare information management systems. They ensure safe record keeping of patient medical records along with clinical risk assessment information, details of recommended lab tests and medication as part of preventative care measure. Patient triage systems help clinicians optimise the referral process and enable them to utilise their consultation time more efficiently by focussing on providing more direct care for their patients.
Summary of Chapters
1 INTRODUCTION: Outlines the importance of clinical data for health learning and introduces the hybrid framework proposed to address limitations in conventional systems.
2 LITERATURE REVIEW: Reviews existing clinical decision support systems, ontology-driven frameworks, and machine learning techniques applied in cardiovascular care.
3 A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care: Presents the overall architecture of the integrated system and its two key components: ODCRARS and MLDPS.
4 Ontology Driven Clinical Risk Assessment and Recommendation System (ODCRARS) for Cardiovascular Preventative Care: Details the design and implementation of context-aware information collection, semantic profiling, and the clinical rules engine.
5 Machine Learning Driven Prognostic System (MLDPS) for Cardiovascular Preventative Care: Describes the methodology for developing prognostic models, including data acquisition, preprocessing, feature selection, and validation using clinical datasets.
6 CONCLUSIONS AND FUTURE WORK: Summarizes the contributions of the research and suggests future research directions, such as incorporating Fuzzy Cognitive Maps and active learning strategies.
Keywords
Clinical Decision Support Systems, CDSS, Ontology, Machine Learning, Cardiovascular Care, Prognostic Modeling, Risk Assessment, Semantic Profile, Feature Selection, Evidence-Based Medicine, Big Data, Healthcare Analytics, Diagnostic Support, Patient-Centric Care
Frequently Asked Questions
What is the core purpose of this research?
The research aims to create a hybrid clinical decision support framework that combines expert-driven ontological knowledge with data-driven machine learning to improve cardiovascular preventative care.
What are the two primary components of the proposed system?
The two main components are the Ontology-Driven Clinical Risk Assessment and Recommendation System (ODCRARS) and the Machine Learning Driven Prognostic System (MLDPS).
How is patient information handled in the system?
Information is collected through an ontology-driven, context-aware adaptive questionnaire, which is then transformed into a Patient Semantic Profile to ensure data interoperability and intrinsic meaning.
Which scientific methods are utilized for prognostic modeling?
The system uses various machine learning classifiers, including Logistic Regression, Decision Trees, and Support Vector Machines, alongside feature selection techniques like SFFS and mRMR.
What is the role of the Clinical Rules Engine?
The rules engine acts as a core mechanism to provide cardiac risk assessment scores and control the patient flow within the integrated healthcare solution based on clinical guidelines.
What are the key keywords associated with this thesis?
Key terms include Clinical Decision Support Systems (CDSS), Ontology, Machine Learning, Cardiovascular Care, Prognostic Modeling, and Risk Assessment.
How were the prognostic models validated?
The models were validated using real clinical datasets, including the Rapid Access Chest Pain Clinic (RACPC) data from Raigmore Hospital, as well as heart disease and breast cancer datasets, with input from clinical domain experts.
What is the significance of the "adaptive questionnaire" approach?
The adaptive questionnaire uses a decision tree-based logic to mimic clinical investigative behavior, ensuring that only relevant questions are asked to the patient, thereby improving usability and data quality.
- Arbeit zitieren
- Kamran Farooq (Autor:in), 2015, A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care, München, GRIN Verlag, https://www.grin.com/document/334172
