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Authors: Rui Miguel Dos Santos Patornilho 1 and André Vasconcelos 2

Affiliations: 1 Instituto Superior Técnico, Portugal ; 2 Instituto Superior Técnico, Instituto de Engenharia de Sistemas e Computadores and Investigação e Desenvolvimento, Portugal

ISBN: 978-989-758-298-1

Keyword(s): Diagnosis, Recommendation, Health, Interactivity, Reliability.

Abstract: Today’s health has a determinant role and it is a subject of concern by society. Diagnosing a disease or obtaining a medical specialty, given a set of symptoms, is not a trivial task and different decisions and approaches can be adopted to solve and handle this problem. Expert systems advise patients about a possible diagnosis, associated diseases, treatments and more concrete information about a disease considering simple symptoms. However, most systems don’t have the recommendation component of a medical doctor, which will be the differentiating factor of this research. The aim of this paper is to develop an algorithm capable of determining the medical specialties associated with a set of symptoms and diseases, and based on the medical specialties obtained, recommend the most suitable specialists. The algorithm is divided into two phases: Health Screening and Health Professional Recommendation. Health Screening has the purpose of determining and computing all the medical s pecialties probabilities, given a set of patient symptoms and applying a statistical model based on all the relations symptom!disease and disease!medical specialty. Health Professional Recommendation has the purpose of recommending the best health professionals, given a set of patient preferences, applying a weighted mean average, where each weight of a health professional feature is given by a patient according to his preferences. This algorithm was evaluated through a set of test cases, having a database with information about symptoms, diseases and medical specialties. This algorithm was later compared to other systems that have the same purpose, to access its quality. The comparison result between the algorithm and WebMD system indicates that the diseases found by the solution are in 80% of all the cases equal to the diseases found and pointed by WebMD system. (More)

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Paper citation in several formats:
Miguel Dos Santos Patornilho, R. and Vasconcelos, A. (2018). MedClick Health Recommendation Algorithm - Recommending Healthcare Professionals Handling Patient Preferences and Medical Specialties.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-298-1, pages 387-396. DOI: 10.5220/0006672103870396

@conference{iceis18,
author={Rui Miguel Dos Santos Patornilho. and André Vasconcelos.},
title={MedClick Health Recommendation Algorithm - Recommending Healthcare Professionals Handling Patient Preferences and Medical Specialties},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2018},
pages={387-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006672103870396},
isbn={978-989-758-298-1},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - MedClick Health Recommendation Algorithm - Recommending Healthcare Professionals Handling Patient Preferences and Medical Specialties
SN - 978-989-758-298-1
AU - Miguel Dos Santos Patornilho, R.
AU - Vasconcelos, A.
PY - 2018
SP - 387
EP - 396
DO - 10.5220/0006672103870396

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