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Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical
Nicholas J Dobbins1, Clifford H Spital2, Robert A Black3
1Department of Biomedical Informatics and Medical Education, UW Medicine Research IT, University of Washington, Seattle, Washington, USA.
Journal of the American Medical Informatics Association : JAMIA
|October 9, 2019
Summary
Leaf is a self-service web application enabling direct querying of clinical data from enterprise data warehouses. This tool empowers users to access information from heterogeneous data models without informatician assistance, supporting health system analytics and research.
Area of Science:
- Health Informatics
- Biomedical Data Science
- Clinical Data Management
Background:
- Academic medical centers face challenges in the secondary use of clinical data.
- Enterprise data warehouses (EDWs) require informatician expertise for data extraction, limiting end-user access.
- Existing cohort discovery tools often mandate specific data models, hindering flexibility.
Purpose of the Study:
- To develop Leaf, a lightweight, self-service web application for querying clinical data.
- To enable direct end-user access to information within heterogeneous data models and sources.
- To overcome the limitations of traditional data extraction methods for clinical data analysis.
Main Methods:
- Leaf employs a flexible biomedical concept system with hierarchical concepts and ontologies.
- Concepts include textual representations and SQL query building blocks accessible via a drag-and-drop interface.
- The application generates abstract syntax trees compiled into dynamic SQL queries.
Main Results:
- Leaf is a production-supported tool at the University of Washington with over 300 active users.
- The application successfully queries a central EDW with heterogeneous data models.
- Leaf source code is publicly available, promoting wider adoption and development.
Conclusions:
- Leaf facilitates simultaneous querying of single or multiple clinical databases, regardless of data model.
- It offers fast installation without data extraction or duplication, reducing costs.
- Leaf is suitable for health system analytics, research data warehouses, precision medicine, and large cohort studies.
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