COVID19PREDICTOR: KLİNİK VERİLERE VE RUTİN TESTLERE DAYALI OLARAK COVID-19 TEŞHİSİ İÇİN MAKİNE ÖĞRENİMİ MODELLERİ GELİŞTİRMEYE YARAYAN WEB TABANLI ARAYÜZ

Year 2022, Volume: 3 Issue: 3, 216 - 221, 31.12.2022

Volkan Kapucu Sultan Turhan Metin Pıçakçıefe Eralp Doğu

https://doi.org/10.52831/kjhs.1117894

Abstract

Amaç: Covid-19 salgını sağlıkla ilgili, sosyal, ekonomik ve bireysel etkiler nedeniyle birçok ülkenin birincil sağlık sorunu haline gelmiştir. Salgın tahmin modellerinin geliştirilmesinin yanı sıra hastalığın risk faktörlerinin incelenmesi ve teşhise yönelik modellerin geliştirilmesi büyük önem taşımaktadır. Bu çalışma, rutin laboratuvar test sonuçları, risk faktörleri, birlikte var olan sağlık koşullarına ilişkin bilgiler gibi klinik verilere dayalı olarak Covid-19'u teşhis etmek için makine öğrenimi yaklaşımlarının kullanıldığı bir iş akışı olan Covid19PredictoR arayüzünü tanıtmaktadır.
Yöntem: Covid19PredictoR arayüzü, R/Shiny'de (https://biodatalab.shinyapps.io/Covid19PredictoR/) açık kaynaklı web tabanlı bir arayüzdür. Sistem içerisinde lojistik regresyon, C5.0, karar ağacı, rastgele orman ve XGBoost modelleri geliştirilebilir. Bu modeller aynı zamanda tahmin amacıyla da kullanılabilir. Model geliştirme sırasında ek olarak tanımlayıcı istatistikler, veri ön işleme ve model ayarlama adımları sağlanır.
Bulgular: Einsteindata4u veri seti, Covid19PredictoR arayüzü ile analiz edildi. Bu örnekle, arayüzün eksiksiz çalışması ve iş akışının tüm adımlarının gösterimi aktarıldı. Veri seti için yüksek performanslı makine öğrenme modelleri geliştirilmiş ve tahmin için en iyi modeller kullanıldı. Model başına vaka için özelliklerin analizi ve görselleştirilmesi (yaş, kabul verileri ve laboratuvar testleri) yapıldı.
Sonuç: Covid-19 hastalığını, ilgili risk faktörleri açısından değerlendirmek için makine öğrenimi algoritmalarının kullanımı, hızla artmaktadır. Bu algoritmaların çeşitli platformlarda uygulanması, uygulama zorlukları, tekrarlanabilirlik ve tekrar üretilebilirlik sorunları yaratmaktadır. Arayüz ile standart bir iş akışına dönüştürülen, tasarlanmış bu işlem zinciri, çeşitli geçmiş deneyimlere sahip sağlık uzmanlarının rahatlıkla kullanabileceği ve raporlayabileceği kullanıcı dostu bir yapı sunar.

Keywords

Covid-19, Makine Öğrenmesi, Shiny, Arayüz

COVID19PREDICTOR: WEB-BASED INTERFACE TO DEVELOP MACHINE LEARNING MODELS FOR DIAGNOSIS OF COVID-19 BASED ON CLINICAL DATA AND ROUTINE TESTS

Abstract

Objective: The Covid-19 outbreak has become the primary health problem of many countries due to health related, social, economic and individual effects. In addition to the development of outbreak prediction models, the examination of risk factors of the disease and the development of models for diagnosis are of high importance. This study introduces the Covid19PredictoR interface, a workflow where machine learning approaches are used for diagnosing Covid-19 based on clinical data such as routine laboratory test results, risk factors, information on co-existing health conditions.
Method: Covid19PredictoR interface is an open source web based interface on R/Shiny (https://biodatalab.shinyapps.io/Covid19PredictoR/). Logistic regression, C5.0, decision tree, random forest and XGBoost models can be developed within the framework. These models can also be used for predictive purposes. Descriptive statistics, data pre-processing and model tuning steps are additionally provided during model development.
Results: Einsteindata4u dataset was analyzed with the Covid19PredictoR interface. With this example, the complete operation of the interface and the demonstration of all steps of the workflow have been shown. High performance machine learning models were developed for the dataset and the best models were used for prediction. Analysis and visualization of features (age, admission data and laboratory tests) were carried out for the case per model.
Conclusion: The use of machine learning algorithms to evaluate Covid-19 disease in terms of related risk factors is rapidly increasing. The application of these algorithms on various platforms creates application difficulties, repeatability and reproducibility problems. The proposed pipeline, which has been transformed into a standard workflow with the interface, offers a user-friendly structure that healthcare professionals with various background can easily use and report.

Keywords

Covid-19, Machine Learning, Shiny, Interface

References

• Taylor CHV, Johnson M. Wuhan 2019 Novel Coronavirus - 2019-nCoV. Materials and Methods. 2020;10.
• WHO. Coronavirus disease (Covid-19) pandemic; 2021 [cited 2022 March 11]. Available from: https://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/novel-coronavirus-2019-ncov.
• Udugama B, Kadhiresan P, Kozlowski HN, et al. Diagnosing Covid-19: the disease and tools for detection. ACS Nano. 2020;14(4):3822-3835.
• Rotzinger DC, Beigelman-Aubry C, Von Garnier C, Qanadli S. Pulmonary embolism in patients with Covid-19: time to change the paradigm of computed tomography. Thrombosis Research. 2020;190(C):58-59.
• Morehouse ZP, Samikwa L, Proctor CM, et al. Validation of a direct-to-PCR Covid-19 detection protocol utilizing mechanical homogenization: A model for reducing resources needed for accurate testing. PLoS ONE. 2021;16(8):e0256316.
• Li WT, Ma J, Shende N, et al. Using machine learning of clinical data to diagnose Covid-19: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making. 2020;20(1):247.
• Batista AFM, Miraglia JL, Donato THR, Chiavegatto Filho ADP. Covid-19 diagnosis prediction in emergency care patients: a machine learning approach. MedRxiv. 2020.
• Alakus TB, Turkoglu I. Comparison of deep learning approaches to predict Covid-19 infection. Chaos, Solitons and Fractals. 2020;140.
• Soltan AA, Kouchaki S, Zhu T, et al. Rapid triage for Covid-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test. Lancet Digit Health. 2021;3(2):e78-e87.
• Yang HS, Hou Y, Vasovic LV, et al. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clinical Chemistry. 2020;66(11):1396-1404.
• Joshi RP, Pejaver V, Hammarlund NE, et al. A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results. Journal of Clinical Virology. 2020;129.
• Kukar M, Gunčar G, Vovko T, et al. Covid-19 diagnosis by routine blood tests using machine learning. Scientific Reports. 2021;11(1):10738.
• Bayat V, Phelps S, Ryono R, et al. A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prediction model from standard laboratory tests. Clinical Infectious Diseases. 2021;73(9):e2901-e2907.
• Wu J, Zhang P, Zhang L, et al. Rapid and accurate identification of Covid-19 infection through machine learning based on clinical available blood test results. MedRxiv. 2020.
• Brinati D, Campagner A, Ferrari D, Locatelli M, Banfi G, Cabitza F. Detection of Covid-19 infection from routine blood exams with machine learning: a feasibility study. Journal of Medical Systems. 2020;44(8):135.
• Tschoellitsch T, Dünser M, Böck C, Schwarzbauer K, Meier J. Machine learning prediction of SARS-CoV-2 polymerase chain reaction results with routine blood tests. Laboratory Medicine. 2021;52(2):146-149.
• Shoer S, Karady T, Keshet A, et al. A prediction model to prioritize individuals for a SARS-CoV-2 Test Built from National Symptom Surveys. Med (NY). 2021;2(2):196-208.
• Tordjman M, Mekki A, Mali RD, et al. Pre-test probability for SARS-Cov-2-related infection score: The PARIS score. PLoS ONE. 2020.
• Cabitza F, Campagner A, Ferrari D, et al. Development, evaluation, and validation of machine learning models for Covid-19 detection based on routine blood tests. Clinical Chemistry and Laboratory Medicine. 2020;59(2):421-431.
• Gladding PA, Ayar Z, Smith K, et al. A machine learning program to identify Covid-19 and other diseases from hematology data. Future Science OA. 2021;7(7).
• Demirarslan M, Suner A. Development of a mobile application by using machine learning methods for the prediction of Covid-19 diagnosis with routine blood tests. Ege Journal of Medicine. 2021;60(4):384-393.
• Alballa N, Al-Turaiki I. Machine learning approaches in Covid-19 diagnosis, mortality, and severity risk prediction: a review. Informatics in Medicine Unlocked. 2021;24.
• Syeda HB, Syed M, Sexton KW, et al. The role of machine learning techniques to tackle Covid-19 Crisis: A systematic review. JMIR Medical Informatics. 2020;9(1).
• Adadi A, Lahmer M, Nasiri S. Artificial Intelligence and Covid-19: A Systematic umbrella review and roads ahead. Journal of King Saud University – Computer and Information Sciences. 2021.
• Shiny from R Studio. shiny: Web Application Framework for R. 2020 [cited 2020 September 7] Available from: https://cran.r-project.org/web/packages/shiny/index.html.
• Schwab P, Schütte AD, Dietz B, Bauer S. predCovid-19: A systematic study of clinical predictive models for Coronavirus Disease 2019. Journal of Medical Internet Research. 2020;22(10).
• Feng C, Wang L, Chen X, et al. A Novel triage tool of artificial intelligence-assisted diagnosis aid system for suspected Covid-19 pneumonia in fever clinics. MedRxiv. 2021.
• AlJame M, Ahmad I, Imtiaz A, Mohammed A. Ensemble learning model for diagnosing Covid-19 from routine blood tests. Informatics in Medicine Unlocked. 2020;21.
• Goodman-Meza D, Rudas A, Chiang JN, et al. A machine learning algorithm to increase Covid-19 inpatient diagnostic capacity. PLoS ONE. 2020;15(9):e0239474.
• Kaggle. Diagnosis of Covid-19 and its clinical spectrum. 2021 [cited 2021 February 16] Available from: https://www.kaggle.com/einsteindata4u/covid19.
• Alves MA, Castro GZ, Oliveira BAS, et al. Explaining machine learning based diagnosis of Covid-19 from routine blood tests with decision trees and criteria graphs. Computers in Biology and Medicine. 2021;132.