Training and validating a treatment recommender with partial verification evidence
We provide a basis for the establishment of decision supporting routines on treatments that have been tested in RCTs but have not yet been deployed clinically. Practitioners can use our approach to train and validate a DSS on new treatments by simply using the RCT data available to them. More work is needed to strengthen the robustness of the predictors. Since there are no further data available to this purpose, but those already used, the potential of synthetic data generation seems an appropriate alternative.
Authors
- Dimitrios Kikidis (ORCID: https://orcid.org/0000-0002-1445-5474)
- José A. López‐Escámez (ORCID: https://orcid.org/0000-0002-8583-1430)
- Clara Puga (ORCID: https://orcid.org/0000-0002-4437-2178)
- Rüdiger Pryss (ORCID: https://orcid.org/0000-0003-1522-785X)
- Vishnu Unnikrishnan (ORCID: https://orcid.org/0000-0002-0086-594X)
- Miro Schleicher (ORCID: https://orcid.org/0000-0002-5987-5325)
- Birgit Mazurek (ORCID: https://orcid.org/0000-0002-9587-6632)
- Rilana Cima
- Berthold Langguth (ORCID: https://orcid.org/0000-0002-7066-510X)
- Winfried Schlee (ORCID: https://orcid.org/0000-0001-7942-1788)
- Uli Niemann (ORCID: https://orcid.org/0000-0001-9634-2248)
- Eleftheria Vellidou
- Myra Spiliopoulou (ORCID: https://orcid.org/0000-0002-1828-5759)
- Stefan Schoisswohl (ORCID: https://orcid.org/0000-0003-3633-999X)
Institutions
- Universidad de Granada (ES)
- National and Kapodistrian University of Athens (GR)
- Humboldt-Universität zu Berlin (DE)
- Universität der Bundeswehr München (DE)
- Zimmer Biomet (Germany) (DE)
- OST - Ostschweizer Fachhochschule (CH)
- Institute of Communication and Computer Systems (GR)
- University of Applied Sciences St. Gallen (CH)
- Adelante (NL)
- Hippocration General Hospital (GR)
- University of Regensburg (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
Publication Details
- Journal
- Artificial Intelligence in Medicine
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1016/j.artmed.2024.103062
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission