All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

A Pipeline for Population and Analysis of Personal Health Knowledge Graphs (PHKGs)

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F22%3A00364139" target="_blank" >RIV/68407700:21460/22:00364139 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/22:00364139

  • Result on the web

    <a href="https://ceur-ws.org/Vol-3235/paper8.pdf" target="_blank" >https://ceur-ws.org/Vol-3235/paper8.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Pipeline for Population and Analysis of Personal Health Knowledge Graphs (PHKGs)

  • Original language description

    Personal Health Knowledge Graphs (PHKGs) are not yet ubiquitous, even though they have a great potential to enrich general knowledge captured in various Knowledge Graphs by adding personal contexts. This poster paper presents work in progress about a pipeline for generating PHKGs from tree-structured Electronic Health Record (EHR) data by applying a hierarchical ontological approach. This pipeline could also be applied to other domains of Personal Knowledge Graphs. Moreover, this pipeline targets the intersection between the symbolic representation of knowledge used for computational semantics and numeric graph data representation used for graph analysis and machine learning. We present the first results from applying this pipeline to synthetic patient EHRs with the diagnosis of colorectal cancer (based on Synthea). The resulting numeric representation of PHKGs or their subgraphs can be used in many practical graph algorithms. Finally, our pipeline study uncovers future research on how this numeric representation of PHKGs should be embedded into continuous and low-dimensional vector space to utilize graph machine learning and deep learning methods

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    roceedings of Poster and Demo Track and Workshop Track of the 18th International Conference on Semantic Systems co-located with 18th International Conference on Semantic Systems (SEMANTiCS 2022)

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

    1613-0073

  • Number of pages

    5

  • Pages from-to

  • Publisher name

    CEUR Workshop Proceedings

  • Place of publication

    Aachen

  • Event location

    Vienna

  • Event date

    Sep 13, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article