Information retrieval is a major challenge in medical informatics. Various research projects have worked on this task in recent years on an institutional level by developing tools to integrate and retrieve information. However, when it comes down to querying such data across institutions, the challenge persists due to the high heterogeneity of data and differences in software systems. The German Biobank Node (GBN) project faced this challenge when trying to interconnect four biobanks to enable distributed queries for biospecimens. All biobanks had already established integrated data repositories, and some of them were already part of research networks. Instead of developing another software platform, GBN decided to form a bridge between these. This paper describes and discusses a core component from the GBN project, the OmniQuery library, which was implemented to enable on-the-fly query translation between heterogeneous research infrastructures.
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Annu Int Conf IEEE Eng Med Biol Soc
July 2023
Supervised machine learning (ML) is revolutionising healthcare, but the acquisition of reliable labels for signals harvested from medical sensors is usually challenging, manual, and costly. Active learning can assist in establishing labels on-the-fly by querying the user only for the most uncertain -and thus informative- samples. However, current approaches rely on naive data selection algorithms, which still require many iterations to achieve the desired accuracy.
View Article and Find Full Text PDFJ Med Internet Res
October 2023
University Institute of Clinical Chemistry, University Hospital Bern, Bern, Switzerland.
Background: Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2023
Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics. BO hinges on a Bayesian surrogate model to sequentially select query points so as to balance exploration with exploitation of the search space.
View Article and Find Full Text PDFIEEE Trans Vis Comput Graph
January 2023
We propose a simple yet effective method for clustering finite elements to improve preprocessing times and rendering performance of unstructured volumetric grids without requiring auxiliary connectivity data. Rather than building bounding volume hierarchies (BVHs) over individual elements, we sort elements along with a Hilbert curve and aggregate neighboring elements together, improving BVH memory consumption by over an order of magnitude. Then to further reduce memory consumption, we cluster the mesh on the fly into sub-meshes with smaller indices using a series of efficient parallel mesh re-indexing operations.
View Article and Find Full Text PDFProceedings VLDB Endowment
July 2021
Northeastern University, Boston, Massachusetts, USA.
We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity properties: with denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for value of , the top-ranked answers are returned in time. This is within a polylogarithmic factor of , i.
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