Severity and mortality prediction models to triage Indian COVID-19 patients.
As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, openi...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-03-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000020 |
| _version_ | 1797695579135934464 |
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| author | Samarth Bhatia Yukti Makhija Sneha Jayaswal Shalendra Singh Prabhat Singh Malik Sri Krishna Venigalla Pallavi Gupta Shreyas N Samaga Rabi Narayan Hota Ishaan Gupta |
| author_facet | Samarth Bhatia Yukti Makhija Sneha Jayaswal Shalendra Singh Prabhat Singh Malik Sri Krishna Venigalla Pallavi Gupta Shreyas N Samaga Rabi Narayan Hota Ishaan Gupta |
| author_sort | Samarth Bhatia |
| collection | DOAJ |
| description | As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinical outcomes, severity, and mortality, of the patients based on routine non-invasive surveillance of blood parameters from one of the largest cohorts of Indian patients at the day of admission. Patient severity and mortality prediction models achieved 86.3% and 88.06% accuracy, respectively, with an AUC-ROC of 0.91 and 0.92. We have integrated both the models in a user-friendly web app calculator, https://triage-COVID-19.herokuapp.com/, to showcase the potential deployment of such efforts at scale. |
| first_indexed | 2024-03-12T03:14:18Z |
| format | Article |
| id | doaj.art-924bf90576ab442aa7091e8ab569c20a |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T03:14:18Z |
| publishDate | 2022-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-924bf90576ab442aa7091e8ab569c20a2023-09-03T14:14:29ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-03-0113e000002010.1371/journal.pdig.0000020Severity and mortality prediction models to triage Indian COVID-19 patients.Samarth BhatiaYukti MakhijaSneha JayaswalShalendra SinghPrabhat Singh MalikSri Krishna VenigallaPallavi GuptaShreyas N SamagaRabi Narayan HotaIshaan GuptaAs the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinical outcomes, severity, and mortality, of the patients based on routine non-invasive surveillance of blood parameters from one of the largest cohorts of Indian patients at the day of admission. Patient severity and mortality prediction models achieved 86.3% and 88.06% accuracy, respectively, with an AUC-ROC of 0.91 and 0.92. We have integrated both the models in a user-friendly web app calculator, https://triage-COVID-19.herokuapp.com/, to showcase the potential deployment of such efforts at scale.https://doi.org/10.1371/journal.pdig.0000020 |
| spellingShingle | Samarth Bhatia Yukti Makhija Sneha Jayaswal Shalendra Singh Prabhat Singh Malik Sri Krishna Venigalla Pallavi Gupta Shreyas N Samaga Rabi Narayan Hota Ishaan Gupta Severity and mortality prediction models to triage Indian COVID-19 patients. PLOS Digital Health |
| title | Severity and mortality prediction models to triage Indian COVID-19 patients. |
| title_full | Severity and mortality prediction models to triage Indian COVID-19 patients. |
| title_fullStr | Severity and mortality prediction models to triage Indian COVID-19 patients. |
| title_full_unstemmed | Severity and mortality prediction models to triage Indian COVID-19 patients. |
| title_short | Severity and mortality prediction models to triage Indian COVID-19 patients. |
| title_sort | severity and mortality prediction models to triage indian covid 19 patients |
| url | https://doi.org/10.1371/journal.pdig.0000020 |
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