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Speak-PIM, Towards a Framework for the Automatic Detection of Potentially Inappropriate Prescriptions

Authors: Mouazer, Abdelmalek; Sedki, Karima; Tsopra, Rosy; Lamy, Jean-Baptiste;

Speak-PIM, Towards a Framework for the Automatic Detection of Potentially Inappropriate Prescriptions

Abstract

Potentially inappropriate medications (PIMs) have adverse health consequences, particularly in elderly patients. Various explicit criteria have been developed to detect PIMs. However, it is difficult to apply these criteria without the help of an electronic decision support tool. Programming these tools can be very complex. Indeed, for computer scientists it is difficult to understand medical issues and for clinicians it is difficult to program in a computer programming language. In this work we present Speak-PIM, a framework for formalizing the PIM’s rules. Speak-PIM is based on a very simple semantics which is suitable for the declaration of PIMs without embarking on all the complexity of description logic or computer languages. It aims to offer an efficient collaboration between the computer scientists and clinicians.

Country
France
Keywords

MESH: Aged, MESH: Humans, [SDV]Life Sciences [q-bio], Inappropriate Prescribing, Decision Support System, [INFO] Computer Science [cs], Terminology, Computer programming, 004, [SDV] Life Sciences [q-bio], MESH: Inappropriate Prescribing, 617, MESH: Potentially Inappropriate Medication List, Humans, [INFO]Computer Science [cs], Potentially inappropriate medications, Therapeutic guidelines, Potentially Inappropriate Medication List, Aged

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
hybrid