Similarity Search for Richly Annotated Structured Patient Cases

Process-enhanced similarity search has a big potential to improve knowledge discovery and decision support in a number of disciplines, and especially in clinical medicine. Currently, the potential can not be exploited due to a lack of algorithms for both the creation of annotated process representations from unstructured content and of methods for the effective comparison of such annotated processes. In the simpatix project, we focus on the medical domain where the central concept is a patient's case, recorded in a (electronic) health record (EHR). Consisting of mostly unstructured or semi-structured data, such as clinical notes from examinations and treatments, tabularized data from quantitative test (such as blood screenings), or discharge summaries, each case encodes a process describing the individual patient's disease history. This project's main objectives are to a) develop methods for the construction of structured, process-oriented case representations from large data sets including unstructured documents; b) research algorithms for process-enhanced similarity search over richly annotated case collections; and to c) design and implement a generic repository to store process-enhanced case collections that allows scalable, effective similarity search.

Principal Investigators
Starlinger, Johannes (Details) (Knowledge Management in Bioinformatics)

Duration of Project
Start date: 10/2016
End date: 09/2019

Research Areas
Natural Sciences

Last updated on 2021-21-01 at 13:24