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Combined Search in Structured and Unstructured Medical Data

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High-Performance In-Memory Genome Data Analysis

Part of the book series: In-Memory Data Management Research ((IMDM))

Abstract

Today, structured medical data is often considered apart from its unstructured counterpart. When searching for a specific piece of information either structured sources, e.g. genomic variant lists and electronic medical records, or unstructured sources, e.g. medical papers, research documentations and trial descriptions, are examined. However, structured data, such as a patient’s genomic data, can be valuable in searching unstructured data like clinical trial proposals in order to find apposite information for the patient. Consequently, today’s separation of both source types impedes any insights into coherencies between them. In this contribution, I propose utilizing in-memory databases to combine results from search in structured as well as in unstructured medical data and introduce a research prototype for a clinical trial search tool. The prototype suggests matching clinical trials based on a patient’s genome and benefits from the analytical performance of the in-memory database. Furthermore, I investigate how an increasing amount of medical input data affects the performance of the prototype.

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Author information

Authors and Affiliations

  1. Potsdam, Germany

    David Heller

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  1. David Heller
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Correspondence to David Heller .

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Editors and Affiliations

  1. Enterprise Platform and Integration Concepts, Hasso-Plattner-Institute, Potsdam, Germany

    Hasso Plattner

  2. Enterprise Platform and Integration Concepts Chair, Hasso Plattner Institute, Potsdam, Germany

    Matthieu-P. Schapranow

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Heller, D. (2014). Combined Search in Structured and Unstructured Medical Data. In: Plattner, H., Schapranow, MP. (eds) High-Performance In-Memory Genome Data Analysis. In-Memory Data Management Research. Springer, Cham. https://doi.org/10.1007/978-3-319-03035-7_8

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