Recommending What Drug to Prescribe Next for Accurate and Explainable Medical Decisions

Patients with complex diseases (i.e., cancer, diabetes, etc.) often follow a therapeutic that consists of multiple drugs, focusing at different human targets such as genes, proteins, etc. There is already related work in medical research for drug-target prediction and drug re-purposing. In this pape...

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Vydáno v:Proceedings / IEEE International Symposium on Computer-Based Medical Systems s. 213 - 218
Hlavní autoři: Symeonidis, Panagiotis, Chairistanidis, Stergios, Zanker, Markus
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2021
Témata:
Cognition
Drugs
Focusing
graph-based algorithms
Medical treatment
medicine recommendations
meta path-based explanations
Pollution
Proteins
Vaccines
ISSN:2372-9198
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Shrnutí:Patients with complex diseases (i.e., cancer, diabetes, etc.) often follow a therapeutic that consists of multiple drugs, focusing at different human targets such as genes, proteins, etc. There is already related work in medical research for drug-target prediction and drug re-purposing. In this paper, we try to provide both accurate and explainable drug recommendations. In particular, we develop models to help doctors screen candidate drugs and their possible substitutes more comprehensively, by providing also robust explanations. To do this, we build a heterogeneous information network to capture the latent associations between patients, their therapeutics, the drugs used, and diseases nodes by using a meta path-based similarity measure. Based on previous similar patients' historical drug treatments, we can provide personalized drug recommendations along with explanations to support critical medical decisions. Demo code for our hybrid meta-based explanations can be found here. We have performed experiments on three real life data sets, which show that we can increase drastically the explainability of our drug recommendations by using more historical data, whereas the recommendations' accuracy still remains at a high level.
ISSN:2372-9198
DOI:10.1109/CBMS52027.2021.00021