Panacea, a semantic-enabled drug recommendations discovery framework

Charalampos Doulaverakis,Ioannis Kompatsiaris,George Nikolaidis,Athanasios Kleontas

doi:10.1186/2041-1480-5-13

Abstract

BackgroundPersonalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation. Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. Additionally, Semantic Web technologies have been proposed in earlier works, in order to support such systems.ResultsThe paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standard classifications and terminologies, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on a lightweight ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome.ConclusionsPanacea is evaluated both in terms of quality of recommendations against real clinical data and performance. The quality recommendation gave useful insights regarding requirements for real world deployment and revealed several parameters that affected the recommendation results. Performance-wise, Panacea is compared to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modeling and approach to the problem, while also pinpointing the advantages of Panacea. Overall, the paper presents a framework for providing an efficient drug recommendations service where Semantic Web technologies are coupled with traditional business rule engines.

Highlights

Personalized drug prescription can be benefited from the use of intelligent information management and sharing
Research projects funded for enabling Semantic Web technologies in the diagnosis and therapeutic procedures exist such as REMINE [1], PSIP [2], NeOn [3] and Active Semantic Documents [4] or works such as [5], but they don’t fully address the problem of automated drug prescription using drug-drug and drug-disease interactions
This process is performed once offline during initialization and the knowledge base is available to the system for further utilization

Summary

Introduction

Personalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. One of the health sectors where intelligent information management and information sharing compose valuable preconditions for the delivery of top quality services is personalized drug prescription. This is more evident in cases where more than one drug is required to be prescribed, a situation which is not uncommon, as drug interactions may appear.

Methods

Results

Discussion

Conclusion