H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System

2026-08-08

AI Summary

This study addresses the challenges posed by the high heterogeneity of healthcare data and the lack of effective metadata management, which often degrade conventional data lakes into “data swamps,” impeding data interoperability and machine learning (ML) readiness. To overcome these limitations, the authors propose a dual-hybrid semantic data lake architecture that synergistically integrates the dynamic modeling capabilities of knowledge graphs with the metadata generation power of large language models (LLMs). A human-in-the-loop validation mechanism is incorporated to enable automated metadata annotation and high-level semantic alignment. This approach establishes, for the first time, semantic linkages within a data lake explicitly oriented toward ML operability, substantially enhancing the discoverability and computability of heterogeneous medical data while supporting intelligent recommendation of suitable ML methods.

Abstract

Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals. Data lakes are often used in medical data storage to consolidate all heterogeneous diverse data in a single, central location, where it can be saved "as is", without the need to impose a schema like a data warehouse does. Despite their flexibility, though, data lakes are notorious for the "data swamp" failure. Thus, providing a reliable data harmonization mechanism through metadata, without compromising integrity or flexibility, is a real challenge. To this end, knowledge graphs have attracted attention since they provide a dynamic way to depict relationships without a rigid schema-on-write approach. Additionally, another rigorous task relies on the interoperability of data: application of appropriate ML techniques on such a diverse nature of data is not an easy task, as a domain expert must decide the efficacy of a method to a specific data type or dataset. Metadata annotation can aid by tagging applicable operations, however this requires manual intervention, not to mention the plethora of existing datasets which lack such information. To tackle both challenges, in this paper, we propose a semantic data lake architecture that promotes data harmonization and incorporates a generative annotation process (i.e. LLMs) of non-labeled metadata collections to support the application of meaningful ML techniques. Building on top of this approach, we create a higher level of knowledge, identifying suitability of data with respect to applicable ML operations based on their data nature...

Problem

• medical data harmonization
• data lake
• metadata annotation
• heterogeneous data
• interoperability

Innovation

• Semantic Data Lake
• LLM-driven Metadata Annotation
• Human-in-the-Loop
• Medical Data Harmonization
• Knowledge Graph