Defining architectures for recommended systems for medical treatment. A Systematic Literature Review

Cristina Jimenez; Ivan Carrera

Autores/as

Cristina Jimenez Escuela Politécnica Nacional
Ivan Carrera Escuela Politécnica Nacional

Palabras clave:

Recommender System, Machine Learning, Assisted Medicine.

Resumen

This paper presents a Systematic Literature Review(SLR) related to recommender system for medical treatment, aswell as analyze main elements that may provide flexible, accurate,and comprehensive recommendations. To do so, a SLR researchmethodology obey. As a result, 12 intelligent recommendersystems related to prescribing medication were classed dependingto specific criteria. We assessed and analyze these medicinerecommender systems and enumerate the challenges. After studyingselected papers, our study concentrated on two researchquestions concerning the availability of medicine recommendersystems for physicians and the features these systems should have.Further research is encouraged in order to build an intelligentrecommender system based on the features analyzed in this work.

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Biografía del autor/a

Cristina Jimenez, Escuela Politécnica Nacional

Jenny Torres es subdecana (e) de la Facultad de Ingeniería en Sistemas de la Escuela Politécnica Nacional (EPN). Obtuvo su doctorado en Informática en la Universidad Pierre y Marie Curie de Francia. En 2009 obtuvo su M.Sc en Ciencias Computacionales en la Universidad Paris-Est Créteil. Antes de ser becaria de la SENESCYT, culminó una maestría en Gerencia de Redes y Telecomunicaciones en la Escuela Politécnica del Ejército y en 2006 se graduó de ingeniera en Sistemas en la EPN.
Su investigación se centra en seguridad informática, gestión de redes, gestión de identidades, redes inalámbricas e infraestructuras abiertas. Fue docente invitada durante seis meses en la Universidad de Paraná, Curitiba, Brasil y forma parte de los equipos de investigación Phare y NR2 en Francia y Brasil respectivamente.

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