Development and utilization of an intelligent application for aiding COVID-19 diagnosis

Meng, Zhiyi; Wang, Minjin; Song, Huan; Guo, Shuo; Liu, Weimin; Zhou, Yongzhao; Li, Mengjiao; Song, Xingbo; Zhou, Yi; Li, Qingfeng; Lu, Xiaojun; Ying, Binwu

doi:10.1101/2020.03.18.20035816

Cited by 44 publications

(73 citation statements)

References 8 publications

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“…8 So far only one study analyzed diagnosis of COVID-19 with routinely-collected data, in this case to predict cases using as controls patients with viral pneumonia by applying logistic regression. 9 Another study of 53 patients from two hospitals in Wenzhou, China, analyzed the accuracy of five machine learning algorithms to predict Acute Respiratory Distress Syndrome (ARDS) in patients with COVID-19. 10 Two other studies applied machine learning algorithms to predict mortality in patients with COVID-19, using patient data from Kaggle and China.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 diagnosis prediction in emergency care patients: a machine learning approach

Batista

et al. 2020

Preprint

114

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The coronavirus disease pandemic has increased the necessity of immediate clinical decisions and effective usage of healthcare resources. Currently, the most validated diagnosis test for COVID-19 (RT-PCR) is in shortage in most developing countries, which may increase infection rates and delay important preventive measures. The objective of this study was to predict the risk of positive COVID-19 diagnosis with machine learning, using as predictors only results from emergency care admission exams. We collected data from 235 adult patients from the Hospital Israelita Albert Einstein in São Paulo, Brazil, from 17 to 30 of March, 2020, of which 102 (43%) received a positive diagnosis of COVID-19 from RT-PCR tests. Five machine learning algorithms (neural networks, random forests, gradient boosting trees, logistic regression and support vector machines) were trained on a random sample of 70% of the patients, and performance was tested on new unseen data (30%). The best predictive performance was obtained by the support vector machines algorithm (AUC: 0.85; Sensitivity: 0.68; Specificity: 0.85; Brier Score: 0.16). The three most important variables for the predictive performance of the algorithm were the number of lymphocytes, leukocytes and eosinophils, respectively. In conclusion, we found that targeted decisions for receiving COVID-19 tests using only routinely-collected data is a promising new area with the use of machine learning algorithms.

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Section: Discussionmentioning

confidence: 99%

COVID-19 diagnosis prediction in emergency care patients: a machine learning approach

Batista

et al. 2020

Preprint

114

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“…In addition, we reached out to authors to include studies that were not publicly available at the time of the search, 7 8 and included studies that were publicly available but not on the Living Systematic Review 6 list at the time of our search. [9][10][11][12] Databases were initially searched on 13 th March 2020, with an update on 24 th March 2020. All studies were considered, regardless of language or publication status (preprint or peer reviewed articles).…”

Section: Methodsmentioning

confidence: 99%

“…One study developed a model to detect COVID-19 pneumonia in fever clinic patients (estimated C-index 0.94), 10 one to diagnose COVID-19 in suspected cases (estimated C-index 0.97), 30 one to diagnose COVID-19 in suspected and asymptomatic cases (estimated C-index 0.87), 12 one to diagnose COVID-19 using deep learning of genomic sequences (estimated Cindex 0.98), 35 and one to diagnose severe disease in symptomatic paediatric inpatients based on direct bilirubin and alaninetransaminase (reporting an F1 score of 1.00 , indicating 100% observed sensitivity and specificity). 24 Only one study reported assessing calibration, but it was unclear how this was done.…”

Section: Diagnostic Models To Detect Covid-19 Infection In Symptomatimentioning

confidence: 99%

“…The guarantors had full access to all the data in the study, take responsibility for the integrity of the data and the accuracy of the data analysis, and had final responsibility for the decision to submit for publication. 35 unclear low low high Meng, Wang, et al 12 high low high high Song, Xu, et al 30 high unclear low high Yu, Shao, et al 24 unclear unclear unclear high…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants

Calster

Bonten

et al. 2020

Preprint

167

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words)Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24 th March 2020. Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST.Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results 2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China.Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in

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“…In the present manuscript, we describe this population and highlight the differences between patients who actually tested positive to SARS-COV-2 and those who did not. Few attempts in applying arti cial intelligence torapidly predict positivity/negativity to SARS-COV-2 were made since the outbreak, using mostly CT imaging and lab results, collected in Chinese population (42)(43)(44). Nevertheless, we present the rst European attempt and promising results applying arti cial intelligence to rapidly predict positivity/negativity to SARS-COV-2 using only basic clinical data, available in the vast majority of emergency departments all over the world.…”

Section: Discussionmentioning

confidence: 99%

Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

Länger¹,

Favarato²,

Giudici³

et al. 2020

Preprint

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Abstract Objective: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our maingoal was assessing the accuracy of artificial intelligence in forecasting the resultsof RT-PCR for SARS-COV-2, using basic information at hand in all emergencydepartments.Methods: This is a retrospective study carried out between February 22 and March 16 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2.Patients under 12 years old, with no leukocyte formula performed in the ED,were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission.Results: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed,on a larger-scale study, this approach could have important clinical and organizational implications.

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Development and utilization of an intelligent application for aiding COVID-19 diagnosis

Cited by 44 publications

References 8 publications

COVID-19 diagnosis prediction in emergency care patients: a machine learning approach

COVID-19 diagnosis prediction in emergency care patients: a machine learning approach

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

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