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"Annotation and Structuring of Patient Cases for Similarity Search"

Johannes Starlinge, HU Berlin

29-May-2017, 4 pm

Location:  DIMA, TU Berlin, E-N 719

Abstract: In the simpatix project, we investigate similarity search over electronic health records. These records consist of mostly unstructured or semi-structured data, such as clinical notes from examinations and treatments, tabularized data from quantitative tests (such as blood screenings), or discharge summaries. This data encodes an implicit process describing the individual patient’s disease history. In simpatix, we extract this process from EHRs, together with rich annotations of clinically relevant entities (e.g., diagnoses, treatments, or procedures), and investigate similarity measures for such process-structured case representations to compare and find similar cases. In the end, we want to deploy these measures in similarity search over large collections of patient cases to enable use cases such as clinical decision support. This talk gives an overview of the project, conceptional technical challenges, and data sources currently accessed.

Bio: Dr.-Ing. Johannes Starlinger is an MD and a postdoctoral computer scientist at the Department of Computer Science at Humboldt-Universität zu Berlin where he works in the research group for Knowledge Management in Bioinformatics. After studying medicine at Medical University of Vienna and computer science at HU-Berlin, Dr. Starlinger joined the DFG-funded graduate program SOAMED in 2010 to research service-oriented architectures in a medical area of application, receiving his PhD in 2015. His current research focus is on knowledge mining and similarity search over data relevant to the biomedical domain, including scientific workflows, genomic data, and medical data. As technical coordinator in the PREDICT project, Dr. Starlinger researches and develops data integration systems to assist precision oncology. As PI on the simpatix project, he investigates process-oriented analysis and similarity assessment of patient cases and disease histories.