Bachelor/Master Thesis: Unsupervised FHIR Questionnaire Template Checker

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Background

The COVID-19 pandemic has revealed the necessity and potential of digital systems in helping researchers tracking symptoms and other biomarkers of the population. The NUM-COMPASS project was funded to create a unified framework that helps researchers with developing apps for pandemic studies. The scientific discussion within the different university hospitals in the NUM network yielded the idea of a template checker for Fast Healthcare Interoperability Resources (FHIR)-based questionnaires. The FHIR standard offers researchers a unified way to design questions for use in bigger study contexts involving questionnaires from different users around Germany. To add new questions to the framework, they have to conform to a template. To check such conformity and assist researchers during questionnaire design, the thesis aims at developing an automated pipeline based on deep learning and Natural Language Processing (NLP). Such an algorithm would learn answer types that conform to the FHIR-based framework and recommend it to the researcher during study design. The algorithm should also recommend complementary questions that conform to the framework based on specified question types.

Aim

Create an automated question template checker for FHIR-based pandemic questionnaires.

Data

Project type Master thesis / Bachelor thesis
ECTS 30/10
Language English and/or German
Period Summer term 21
Presence time Working from remote
Useful knowledge Natural Language Processing, Deep Learning, Pandemic Apps, FHIR
Work distribution 100% programming of NLP algorithms and FHIR data
Registration E-Mail to david.kopyto@fau.de

Literature

Literature will be provided in the first meeting and the candidate is encouraged to further research relevant papers for this work.

Examination

Thesis report and final presentation.

Contact

David Kopyto

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
    Germany
  • Phone number: +49 9131 85-23608
  • Email: david.kopyto@fau.de

Dr. Luis I. Lopera G.

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
    Germany
  • Phone number: +49 9131 85-23605
  • Email: luis.i.lopera@fau.de

Friedrich-Alexander-Universität Erlangen-Nürnberg