Conquery: an open source application to analyze high content healthcare data
Fabian Kovacs, Max Thonagel, Marion Ludwig, Alexander Albrecht, Manuel, Hegner, Dirk Enders, Lennart Hickstein, Maximilian von Knobloch, Anne, Rothhardt, Jochen Walker

TL;DR
Conquery is an open-source platform that simplifies complex, resource-efficient analysis of large healthcare datasets for non-technical medical professionals, enhancing decision-making in healthcare.
Contribution
It introduces a document-oriented distributed timeseries database with an intuitive interface and a custom compression scheme for fast, resource-efficient analysis of medical records.
Findings
Enables easy navigation and cohort construction in healthcare data
Reduces time and resource demands for data analysis
Supports decision-making in healthcare through intuitive tools
Abstract
Introduction: Big data in healthcare must be exploited to achieve a substantial increase in efficiency and competitiveness. Especially the analysis of patient-related data possesses huge potential to improve decision-making processes. However, most analytical approaches used today are highly time- and resource-consuming. Objectives: The presented software solution Conquery is an open-source software tool providing advanced, but intuitive data analysis without the need for specialized statistical training. Conquery aims to simplify big data analysis for novice database users in the medical sector. Methods: Conquery is a document-oriented distributed timeseries database and analysis platform. Its main application is the analysis of per-person medical records by non-technical medical professionals. Complex analyses are realized in the Conquery frontend by dragging tree nodes into the query…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Healthcare · Electronic Health Records Systems · Clinical practice guidelines implementation
