Research Area: Machine Learning
Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. However, physicians can reduce their workload by using recommender systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters.
Keywords:
digital transformation
medical domain
recommender system
physiological parameter
deep learning
Author(s) Name: Juan G. Diaz Ochoa, Orsolya Csiszar, Thomas Schimper
Journal name: BMC Medical Informatics and Decision Making
Conferrence name:
Publisher name: Springer
DOI: https://doi.org/10.1186/s12911-021-01553-3
Volume Information: Volume 21
