This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a common data model. The GEM system automates data mapping by providing precise suggestions for data element mappings. It leverages the detailed metadata about elements in associated dataset documentation such as data dictionaries that are typically available with biomedical datasets. It employs unsupervised text mining techniques to determine similarity between data elements and also employs machine-learning classifiers to identify element matches. It further provides an active-learning capability where the process of training the GEM system is optimized. Our experimental evaluations show that the GEM system provides highly accurate data mappings (over 90% accuracy) for real datasets of thousands of data elements each, in the Alzheimer's disease research domain. Further, the effort in training the system for new datasets is also optimized. We are currently employing the GEM system to map Alzheimer's disease datasets from around the globe into a common representation, as part of a global Alzheimer's disease integrated data sharing and analysis network called GAAIN1. GEM achieves significantly higher data mapping accuracy for biomedical datasets compared to other state-of-the-art tools for database schema matching that have similar functionality. With the use of active-learning capabilities, the user effort in training the system is minimal.

We present a software system solution that significantly simplifies data sharing of medical data. This system, called GEM (for the GAAIN Entity Mapper), harmonizes medical data. Harmonization is the process of unifying information across multiple disparate datasets needed to share and aggregate medical data. Specifically, our system automates the task of finding corresponding elements across different independently created (medical) datasets of related data. We present our overall approach, detailed technical architecture, and experimental evaluations demonstrating the effectiveness of our approach.

We provide a working demonstration of a software tool that we have developed for schema-matching across multiple, heterogeneous datasets of Alzheimer's disease data.

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