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Systematic Literature Review and Ontology Development for Contextual Information in Concept Models
This project aims to systematically explore how "context" is conceptualized and represented within conceptual models across various Therapeutic Areas (TAs) in clinical research. The goal is to review existing literature, analyze qualitative research, and identify common themes and differences in how context, environment, and adaptation are integrated into these models. The project will result in a comprehensive review paper, the development of an initial ontology, and a gap analysis of existing datasets and sensors used to capture contextual information.
Keywords: Concept models, context, clinical research, qualitative research
Context (i.e. the surroundings and setting in which clinical evidence is measured) is critical in influencing how clinical outcomes are interpreted. However, there is variability in how context is defined and integrated into conceptual models across different TAs and conditions. By systematically reviewing existing literature and datasets, this project aims to standardize the understanding and representation of context, thereby improving the robustness and applicability of conceptual models, and creating a foundation for the measurement of contextual information which can be used in the interpretation and modeling of clinical evidence.
The ideal candidate would _**have or be interested to learn the following skill sets**_:
1. Systematic Literature Review: Conduct thorough reviews and synthesize findings from diverse sources.
2. Qualitative Analysis: Analyzing qualitative data across different domains. Thematic analysis.
3. Ontology Development: Familiarity with ontology design and data integration.
4. Data and sensor technologies: Knowledge of sensors, data sources, and their application in clinical outcome modeling.
Context (i.e. the surroundings and setting in which clinical evidence is measured) is critical in influencing how clinical outcomes are interpreted. However, there is variability in how context is defined and integrated into conceptual models across different TAs and conditions. By systematically reviewing existing literature and datasets, this project aims to standardize the understanding and representation of context, thereby improving the robustness and applicability of conceptual models, and creating a foundation for the measurement of contextual information which can be used in the interpretation and modeling of clinical evidence.
The ideal candidate would _**have or be interested to learn the following skill sets**_:
1. Systematic Literature Review: Conduct thorough reviews and synthesize findings from diverse sources.
2. Qualitative Analysis: Analyzing qualitative data across different domains. Thematic analysis.
3. Ontology Development: Familiarity with ontology design and data integration.
4. Data and sensor technologies: Knowledge of sensors, data sources, and their application in clinical outcome modeling.
The project will be divided into the following tasks:
_Systematic Literature Review:_
- Conceptual Models: Conduct a systematic review of literature to understand how "context" is described and integrated into conceptual models across different TAs.
- Qualitative Analysis: Examine qualitative research across TAs to identify similarities, differences, sub-themes, and concept groups related to "context" or "environment."
_Ontology Development:_
- Initial Ontology: Develop an initial ontology to integrate contextual data, informed by a literature review of existing approaches.
- Literature Review of Datasets: Perform a systematic review of datasets that capture contextual information to identify those relevant for contextualizing outcomes.
_Gap Analysis:_
- Sensors and Data Sources: Conduct a comprehensive analysis of sensors and data sources (beyond just smartphones) across all TAs, focusing on datasets with anchor outcomes and preferably longitudinal data.
- Mapping Sensor Data to Context Models: Explore how available sensor data maps to the developed concept model of context, identifying gaps and existing capabilities.
- Minimal Dataset for Context: Discuss and propose what a minimal dataset for context might look like across different TAs, highlighting any variations.
The project is expected to result in the following outputs:
_Review Papers:_
- A systematic review paper on conceptual models and context.
- A potential additional paper on sensing apps and public datasets for digital phenotyping of mental health, based on a systematic review.
_Ontology and Concept Model:_
- Design and propose a concept model for context.
- Develop an ontology for describing contextual information, applying it to datasets like StudentLife or others identified during the review.
_Discussion and Recommendations:_
- Discussion paper on the minimal dataset for context across TAs, with a focus on gaps and existing capabilities.
The project will be divided into the following tasks:
_Systematic Literature Review:_
- Conceptual Models: Conduct a systematic review of literature to understand how "context" is described and integrated into conceptual models across different TAs.
- Qualitative Analysis: Examine qualitative research across TAs to identify similarities, differences, sub-themes, and concept groups related to "context" or "environment."
_Ontology Development:_
- Initial Ontology: Develop an initial ontology to integrate contextual data, informed by a literature review of existing approaches.
- Literature Review of Datasets: Perform a systematic review of datasets that capture contextual information to identify those relevant for contextualizing outcomes.
_Gap Analysis:_
- Sensors and Data Sources: Conduct a comprehensive analysis of sensors and data sources (beyond just smartphones) across all TAs, focusing on datasets with anchor outcomes and preferably longitudinal data.
- Mapping Sensor Data to Context Models: Explore how available sensor data maps to the developed concept model of context, identifying gaps and existing capabilities.
- Minimal Dataset for Context: Discuss and propose what a minimal dataset for context might look like across different TAs, highlighting any variations.
The project is expected to result in the following outputs:
_Review Papers:_
- A systematic review paper on conceptual models and context.
- A potential additional paper on sensing apps and public datasets for digital phenotyping of mental health, based on a systematic review.
_Ontology and Concept Model:_
- Design and propose a concept model for context.
- Develop an ontology for describing contextual information, applying it to datasets like StudentLife or others identified during the review.
_Discussion and Recommendations:_
- Discussion paper on the minimal dataset for context across TAs, with a focus on gaps and existing capabilities.
