Risk Diagnosis and Mitigation System of COVID-19 Using Expert System and Web Scraping

Mufid, Mohammad Robihul; Basofi, Arif; Mawaddah, Saniyatul; Khotimah, Khusnul; Fuad, N.

doi:10.1109/ies50839.2020.9231619

Cited by 15 publications

(14 citation statements)

References 8 publications

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“…In the present scoping review, the results of 16 studies identified a positive impact on a diagnosis of COVID-19 with the assistance of CDSS based on ES. 5,17,[86][87][88][89][90][91][92][93][94][95][96][97][98][99] In the reviewed studies, rule sets were the most common methods in developing a knowledge base of CDSS. 5,17,88,89,93,94,96,97 Rule-based systems acquire contextual knowledge from extracted data stored and manipulated in other approaches.…”

Section: Knowledge-based Cdssmentioning

confidence: 99%

“…101 The reviewed studies extracted data from various sources, such as observed symptoms, specific medical measurements, pre-existing medical conditions or any hospitalization history, recent PCR test results, clinical guidelines, websites (e.g., WHO), and knowledge of experts to develop rule sets. 5,17,[86][87][88][89][90][91][92][93][94][95][96][97][98][99] Therefore, knowledge-based CDSS in these studies can provide a comprehensive view of the patient's health information. This feature enables health practitioners to access treatment recommendations and risk classification, recommend test lists, quickly track test results and symptoms, and access clinical guidelines in the COVID-19 pandemic.…”

Section: Knowledge-based Cdssmentioning

confidence: 99%

“…The reviewed studies used different metrics, including diagnostic rate, accuracy, ease of use, and time, to evaluate the performance of knowledge-based CDSS. 5,17,[86][87][88][89][90][91][92][93][94][95][96][97][98][99] The reason for this could be the widespread COVID-19 pandemic, leading to a lack of time to evaluate these systems' performance properly. It is essential for…”

Section: Knowledge-based Cdssmentioning

confidence: 99%

“…Some studies used a combination of ML methods to develop CDSS (n = 14) [34][35][36][37]39,[42][43][44]53,[55][56][57]70,84 (Appendix 2). Rule sets (n = 8), 5,17,88,89,93,94,96,97 fuzzy mathematical models (n = 5), 86,[90][91][92]95 and ontology (n = 3) 87,98,99 were used for developing ICDSS based on ES (knowledge-based CDSS), respectively (Appendix 2).…”

Section: Svmmentioning

confidence: 99%

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Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

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Background and AimsDue to the COVID‐19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID‐19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID‐19.MethodsWe searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID‐19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher.ResultsA search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge‐based CDSS (n = 52) and knowledge‐based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS.ConclusionCDSS for COVID‐19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision‐making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge‐based and knowledge‐based CDSS in COVID‐19 diagnosis.

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Section: Knowledge-based Cdssmentioning

confidence: 99%

Section: Knowledge-based Cdssmentioning

confidence: 99%

Section: Knowledge-based Cdssmentioning

confidence: 99%

Section: Svmmentioning

confidence: 99%

See 2 more Smart Citations

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

View full text Add to dashboard Cite

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“…Mufid et al developed an Android app that consists of an expert system for early detection of having COVID-19 and an information module that displays current news about the spread of the virus [25]. The app was tailored specifically for Indonesia.…”

Section: Related Workmentioning

confidence: 99%

Self-Assessment of Having COVID-19 With the Corona Check mHealth App

Beierle¹,

Allgaier²,

Stupp³

et al. 2023

IEEE J. Biomed. Health Inform.

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At the beginning of the COVID-19 pandemic, with a lack of knowledge about the novel virus and a lack of widely available tests, getting first feedback about being infected was not easy. To support all citizens in this respect, we developed the mobile health app Corona Check. Based on a self-reported questionnaire about symptoms and contact history, users get first feedback about a possible corona infection and advice on what to do. We developed Corona Check based on our existing software framework and released the app on Google Play and the Apple App Store on April 4, 2020. Until October 30, 2021, we collected 51,323 assessments from 35,118 users with explicit agreement of the users that their anonymized data may be used for research purposes. For 70.6% of the assessments, the users additionally shared their coarse geolocation with us. To the best of our knowledge, we are the first to report about such a large-scale study in this context of COVID-19 mHealth systems.Although users from some countries reported more symptoms on average than users from other countries, we did not find any statistically significant differences between symptom distributions (regarding country, age, and sex). Overall, the Corona Check app provided easily accessible information on corona symptoms and showed the potential to help overburdened corona telephone hotlines, especially during the beginning of the pandemic. Corona Check thus was able to support fighting the spread of the novel coronavirus. mHealth apps further prove to be valuable tools for longitudinal health data collection.

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