Evidence map›Paper›PMID 32845437›Full record

ArticleMedical & biological engineering & computing2020

DZC DIAG: mobile application based on expert system to aid in the diagnosis of dengue, Zika, and chikungunya.

Adriana Peter Rodrigues de Araújo, Maria Carolina Macedo de Araujo, Thiago Coutinho Cavalcanti, Cláudia Fernanda de Lacerda Vidal, Marilú Gomes Netto Monte da Silva

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Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Adriana Peter Rodrigues de AraújoBiomedical Engineering Department, University of Southern California, Los Angeles, USA.
Maria Carolina Macedo de AraujoBiomedical Engineering Department, Federal University of Pernambuco, Av. da Arquitetura, s/n - Cidade Universitária, Recife, PE, CEP: 50740-550, Brazil.
Thiago Coutinho CavalcantiInformation and Communication Engineering Department, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea.
Cláudia Fernanda de Lacerda VidalTropical Medicine Department, Federal University of Pernambuco, Recife, Brazil.
Marilú Gomes Netto Monte da SilvaBiomedical Engineering Department, Federal University of Pernambuco, Av. da Arquitetura, s/n - Cidade Universitária, Recife, PE, CEP: 50740-550, Brazil. gomes.marilu@gmail.com.ORCID http://orcid.org/0000-0003-2189-4739

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dengue, Zika, and chikungunya are epidemic diseases transmitted by the Aedes mosquito. These virus infections can be so severe to the point of bringing on mobility and neurological problems, or even death. Expert systems (ES) can be used as tools for the identification of patterns intended to solve problems in the same way as a professional specialist would. This work aimed to develop an ES in the form of an Android application to serve as a supportive tool in the diagnosis of these arboviruses. The goal is to associate the set of symptoms from a patient to a score related to the likelihood of them having these diseases. To make this possible, we implemented a rule-based ES which considers the presence of symptoms itself and the relation between them to associate the case under analysis to others found in the literature. We performed 96 tests (32 for each illness), and our system had a success rate of 96.88%. Resident physicians of a public hospital also analyzed these clinical cases and achieved an average success rate of 72.92%. Comparing the results of the method proposed and errors made by health professionals, we showed an improvement in the effectiveness of clinical diagnoses. Graphical abstract Figure - DZC DIAG Operating Flowchart: the physicians record patients' data and answer a series of questions related to the patient's symptoms; after all the questions, the result is generated by the expert system (score for dengue, Zika, and chikungunya); and it is saved in the same device where the test was done and uploaded online to a FTP.

Indexed as

Expert SystemsBrazilChikungunya FeverDengueDiagnosis, Computer-AssistedDiagnostic ErrorsHumansKnowledge BasesMobile ApplicationsPhysiciansUser-Computer InterfaceZika Virus InfectionChikungunya virusDengueExpert systemmHealthZika virus

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.