Applying In-Memory Technology for Automatic Template Filling in the Clinical Domain

We present a research prototype for systematic template filling based on in-memory database technology. Entity extraction and normalization is based on domain-specific dictionaries and customized rules set building on top of related work of the medical field. The prototype called HPI proves feasibility of in-memory technology to enhance workflows in the field of efficient text processing and analysis. With our approach, the iterative process of dictionary and rule refinement for enhancing text analysis results shifts from a time-consuming task with long waiting hours to a continuous workflow. In the context of the challenge’s task, our prototype achieves an overall average accuracy of 0.769 and an overall F1 measure of up to 0.323.