Development and utilization of an intelligent application for aiding COVID-19 diagnosis
Meng, Z.; Wang, M.; Song, H.; Guo, S.; Zhou, Y.; Li, W.; Zhou, Y.; Li, M.; Song, X.; Zhou, Y.; Li, Q.; Lu, X.; Ying, B.
Show abstract
BackgroundCOVID-19 has been spreading globally since emergence, but the diagnostic resources are relatively insufficient. ResultsIn order to effectively relieve the resource deficiency of diagnosing COVID-19, we developed a machine learning-based diagnosis model on basis of laboratory examinations indicators from a total of 620 samples, and subsequently implemented it as a COVID-19 diagnosis aid APP to facilitate promotion. ConclusionsExternal validation showed satisfiable model prediction performance (i.e., the positive predictive value and negative predictive value was 86.35% and 84.62%, respectively), which guarantees the promising use of this tool for extensive screening.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- ChatGPT-Enhanced ROC Analysis (CERA): A Shiny Web Tool for Finding Optimal Cutoff in Biomarker Analysis 94%
- Detecting Rare Diseases in Electronic Health Records Using Machine Learning and Knowledge Engineering: Case Study of Acute Hepatic Porphyria 94%
- A Risk Prediction and Clinical Guidance System for Evaluating Patients with Recurrent Infections. 92%
Similar papers in this journal
- Performance of an artificial intelligence-based smartphone app for guided reading of SARS-CoV-2 lateral-flow immunoassays 94%
- Initial Experience in Predicting the Risk of Hospitalization of 496 Outpatients with COVID-19 Using a Telemedicine Risk Assessment Tool 91%
- NTT Docomo and Apple mobility data compared as countermeasures against COVID-19 outbreak in Japan 90%
Similar papers in this journal
- Availability and Use of Mobile Health Technology for Disease Diagnosis and Treatment Support by Health Workers in the Ashanti Region of Ghana: A Cross-sectional Survey 92%
- Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications in Kyrgyzstan 91%
- Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions 91%
Similar papers in this journal
- An automated Dashboard to improve laboratory COVID-19 diagnostics management 93%
- Medical Clinical Minds Meet Artificial Intelligence: Italian Physicians' Knowledge, Attitudes, and Concordance between Italian Physicians and AI-Generated Diagnoses. A National Cross-Sectional Study 92%
- Using Machine Learning to Predict Mortality for COVID-19 Patients on Day Zero in the ICU 92%
Similar papers in this journal
- Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction 94%
- Health literacy profiles correlate with participation in primary health care among patients with chronic diseases: A latent profile analysis 91%
- Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand In Turkey, A Modelling Study 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.