Evidence map›Paper›PMID 29560150›Full record

ArticleElectronic physician2017

A fuzzy rule-based expert system for diagnosing cystic fibrosis.

Maryam Hassanzad, Azam Orooji, Ali Valinejadi, Aliakbar Velayati

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PubMed Full text at PMC Publisher
In one paragraph

Article in Electronic physician, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. Article
  6. A Fuzzy Expert System to Predict the Risk of Postpartum Hemorrhage.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2019
    Article
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Maryam HassanzadM.D., Associate Professor, Pediatric Respiratory Disease Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Azam OroojiPh.D. Candidate of Medical Informatics, Department of Health Information Management and Technology, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Ali ValinejadiPh.D. of Health Information Management, Assistant Professor, Social Determinants of Health Research Center, Department of Health Information Technology, Semnan University of Medical Sciences, Semnan, Iran.
Aliakbar VelayatiM.D., Distinguished Professor, Mycobacteriology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFinding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.

objectiveThe objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).

methodsDefining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.

resultsResults showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.

conclusionThe system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.

Indexed as

Cystic fibrosisExpert systemsFuzzy logic

Identifiers

PMID29560150
PMCPMC5843424

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LicenceCC BY-NC-ND
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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.