ArticleScandinavian journal of trauma, resuscitation and emergency medicine2020
Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.
Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Evaluation of Diagnostic Strategies for Identifying SARS-CoV-2 Infection in Clinical Practice: a Systematic Review and Compliance with the Standards for Reporting Diagnostic Accuracy Studies Guideline (STARD).Microbiology spectrum · 2022Pooled it
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.The Cochrane database of systematic reviews · 2022Pooled it
- Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.Analytical chemistry · 2024Article
- Detection of patients with COVID-19 by the emergency medical services in Lombardy through an operator-based interview and machine learning models.Emergency medicine journal : EMJ · 2023Observational
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results.Scientific reports · 2023Article
- Graph data science and machine learning for the detection of COVID-19 infection from symptoms.PeerJ. Computer science · 2023Article
- Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial.Clinical and experimental emergency medicine · 2022Article
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- Article
- A literature review on the analysis of symptom-based clinical pathways: Time for a different approach?PLOS digital health · 2022Article
- Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.Journal of medical systems · 2022Article
- An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.Sensors (Basel, Switzerland) · 2022Article
- Use of an algorithm based on routine blood laboratory tests to exclude COVID-19 in a screening-setting of healthcare workers.PloS one · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies.Seminars in immunopathology · 2021Review
- Machine learning is the key to diagnose COVID-19: a proof-of-concept study.Scientific reports · 2021Article
- High Performance of SARS-Cov-2N Protein Antigen Chemiluminescence Immunoassay as Frontline Testing for Acute Phase COVID-19 Diagnosis: A Retrospective Cohort Study.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReverse 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 main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments.
methodsThis is a retrospective study carried out between February 22, 2020 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 and patients in whom the leukocyte formula was not 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. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol.
resultsAmong 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.
conclusionOur 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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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.