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HERALD: A domain-specific query language for longitudinal health data analytics.

Lena Baum1, Marco Johns1, Armin Müller1

  • 1Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany.

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|October 11, 2024
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Summary

HEALD is a new query language that transforms complex longitudinal health data into easy-to-use cross-sectional tables for research. This tool simplifies data analysis for medical researchers and data scientists.

Keywords:
Data aggregationData analysisData interpretationData visualizationSecondary data analysis

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Area of Science:

  • Health Informatics
  • Data Science
  • Bioinformatics

Background:

  • Longitudinal health data offers significant research potential but is complex to analyze.
  • Extracting cross-sectional data for statistical analysis and machine learning is challenging.
  • Existing tools lack a balance between ease-of-use and comprehensiveness.

Purpose of the Study:

  • Introduce HERALD, a novel domain-specific query language for transforming longitudinal health data into cross-sectional tables.
  • Describe HERALD's concepts, syntax, graphical user interface, and integration with i2b2.
  • Simplify data transformation for medical researchers and data scientists.

Main Methods:

  • HEALD uses a natural language-like syntax for data selection, aggregation, relationship analysis, and filtering with temporal constraints.
  • Queries are executed per patient using a hierarchical concept model to generate tabular output.
  • HEALD supports a nesting mechanism where queries can reference previously generated data points.

Main Results:

  • An open-source implementation includes a HERALD query parser, execution engine, and a web-based user interface.
  • The system can be deployed standalone or integrated as a plugin into environments like i2b2.
  • HEALD simplifies the transformation of longitudinal health data into tables and data matrices for data scientists and machine learning experts.

Conclusions:

  • Dedicated query languages can effectively balance complexity and transformation capabilities for longitudinal health data.
  • HEALD provides a valuable tool for researchers needing to convert longitudinal data into analyzable cross-sectional formats.