Analysis of longitudinal health data provides valuable insights into  disease progression, treatment response, and prevention. However,  transforming this data into cross-sectional tables for statistical  analysis and machine learning remains complex. Existing tools often lack  a balance between usability and functionality, making data extraction  and transformation a challenge for researchers.
 HERALD is a domain-specific query language designed to simplify this  process. Its natural language-inspired syntax supports selection,  aggregation, relationship analysis, filtering, and temporal constraints.  Queries are executed individually for each patient, generating  cross-sectional tables that can be used for further analysis. The system  includes a web-based user interface for query construction, integrated  statistical analysis, and integrates seamlessly with Informatics for  Integrating Biology and the Bedside (i2b2).
 As an open source tool, HERALD provides a practical solution for  transforming longitudinal health data into cross-sectional tables,  making complex data more accessible for medical research.