A knowledge‐based <scp>self‐pre‐diagnosis</scp> system to predict Covid‐19 in smartphone users using personal data and observed symptoms

Ertuğrul, Duygu Çelik; Ulusoy, Demet

doi:10.1111/exsy.12716

Cited by 19 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

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.

show abstract

Section: Knowledge-based Cdssmentioning

confidence: 99%

Section: Knowledge-based Cdssmentioning

confidence: 99%

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

show abstract

“…But for that it is necessary to build time-series dataset with expensive time cost. Another study [6] uses traditional ontology methods to support the analysis and tracking COVID-19 epidemic in Hubei, China. But this method needs the time cost work to build ontology named OntCov19 and its performance to the COVID-19 pandemic has spread fast all over the world, several studies have proposed early triage systems recently.…”

Section: Related Workmentioning

confidence: 99%

Early Triage of COVID-19 patients exploiting Data-Driven Strategies and Machine Learning Techniques

Park

Kim

Seok

et al. 2022

2022 International Conference on Electronics, Information, and Communication (ICEIC)

View full text Add to dashboard Cite

Coronavirus disease (COVID-19) is still affecting the world. In the pandemic situation of the novel infectious disease, early detection of COVID-19 infection and severity for febrile respiratory patients is critical for efficient management of the medical system delivery system with limited medical personnel and facilities. Thus, we propose early triage exploiting data-driven strategical methods and machine learning techniques using the data of 5,628 admitted patients provided by Korea Central Disease Control Headquarters and 50 confirmed cases in Korea University Ansan Hospital. We proved validity of our data-driven strategies with machine learning models accuracy by doing 200 experiments and find out the features that affect COVID-19 through various feature selection in each medical inspection step. As a result, Stage 5 shows the results of blood test could affect to classify critical and severe cases obtaining precision of 0.2, 0.03 higher than without blood test results. But Stage 3 without blood test results achieved the highest accuracy of 0.88 showing possibility of early triage system without blood test. In conclusion, our triage system, based on data-driven strategies and machine learning techniques, can help in early detection and triage of COVID-19 patients.

show abstract

“…In this study, they will compare different ontology query languages and query systems, as well as provide user assessments. Elik Ertuğrul et al [4] presented a pre-diagnosis-based knowledge system to anticipate Covid-19 smartphone users using the data and symptoms they have. The technology not only shares user information with specialists butalso analyzes symptoms as atool to analyze potential Covid-19 hazards.…”

Section: Literature Overviewmentioning

confidence: 99%

Untitled

2022

IJWesT

View full text Add to dashboard Cite

The Covid-19 ontology is to classify the data using a supervised learning approach in machine learning, which has been preprocessed. Afterthe classification is done, with thehelp of opinion mining with decisionmaking, the classified data is stored in the database using semantic webontology using the protégé tool. The data will be retrieved through SPARQL which helps to retrieve complex queries, followed by the output based on the given query. This Covid-19 ontology helps in analyzing the risk factors and treatment plans for the respective individuals i.e., students based on their given details which include diagnosis, symptoms, and vaccination history. The information given by the students can be automatically processed and with the help of SWRL (Semantic Web Rule Language), the risk factor and treatment plans for the students are inferred from the given knowledge.

show abstract

A knowledge‐based self‐pre‐diagnosis system to predict Covid‐19 in smartphone users using personal data and observed symptoms

Cited by 19 publications

References 26 publications

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

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

Early Triage of COVID-19 patients exploiting Data-Driven Strategies and Machine Learning Techniques

Untitled

Contact Info

Product

Resources

About