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BY 4.0 license Open Access Published by De Gruyter September 18, 2019

Neighborhood Optimization for Therapy Decision Support

  • Felix Gräßer EMAIL logo , Hagen Malberg and Sebastian Zaunseder


This work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.

Published Online: 2019-09-18
Published in Print: 2019-09-01

© 2019 by Walter de Gruyter Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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