A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization
Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.
Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups.
Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries.
Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated.
Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger.
Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
|---|---|
| Enthalten in: |
JAMA network open - 4(2021), 10 vom: 01. Okt., Seite e2124946 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yan, Yao [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 25.10.2021 Date Revised 03.04.2024 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1001/jamanetworkopen.2021.24946 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM331743353 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM331743353 | ||
| 003 | DE-627 | ||
| 005 | 20240403234131.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1001/jamanetworkopen.2021.24946 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1362.xml |
| 035 | |a (DE-627)NLM331743353 | ||
| 035 | |a (NLM)34633425 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yan, Yao |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 25.10.2021 | ||
| 500 | |a Date Revised 03.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic | ||
| 520 | |a Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups | ||
| 520 | |a Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries | ||
| 520 | |a Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated | ||
| 520 | |a Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger | ||
| 520 | |a Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 700 | 1 | |a Schaffter, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Bergquist, Timothy |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Prosser, Justin |e verfasserin |4 aut | |
| 700 | 1 | |a Aydin, Zafer |e verfasserin |4 aut | |
| 700 | 1 | |a Jabeer, Amhar |e verfasserin |4 aut | |
| 700 | 1 | |a Brugere, Ivan |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Jifan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Guanhua |e verfasserin |4 aut | |
| 700 | 1 | |a Causey, Jason |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Yuxin |e verfasserin |4 aut | |
| 700 | 1 | |a Bryson, Kevin |e verfasserin |4 aut | |
| 700 | 1 | |a Long, Dustin R |e verfasserin |4 aut | |
| 700 | 1 | |a Jarvik, Jeffrey G |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Christoph I |e verfasserin |4 aut | |
| 700 | 1 | |a Wilcox, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Guinney, Justin |e verfasserin |4 aut | |
| 700 | 1 | |a Mooney, Sean |e verfasserin |4 aut | |
| 700 | 0 | |a DREAM Challenge Consortium |e verfasserin |4 aut | |
| 700 | 1 | |a Jujjavarapu, Chethan |e investigator |4 oth | |
| 700 | 1 | |a Thomas, Jason |e investigator |4 oth | |
| 700 | 1 | |a Gunn, Martin |e investigator |4 oth | |
| 700 | 1 | |a Wu, YiFan |e investigator |4 oth | |
| 700 | 1 | |a Dobbins, Nicholas |e investigator |4 oth | |
| 700 | 1 | |a O'Reilly-Shah, Vikas |e investigator |4 oth | |
| 700 | 1 | |a Teng, Andrew |e investigator |4 oth | |
| 700 | 1 | |a Hammarlund, Noah |e investigator |4 oth | |
| 700 | 1 | |a Nichol, Graham |e investigator |4 oth | |
| 700 | 1 | |a Brandt, Pascal |e investigator |4 oth | |
| 700 | 1 | |a Pejaver, Vikas |e investigator |4 oth | |
| 700 | 1 | |a Britt, Beth |e investigator |4 oth | |
| 700 | 1 | |a Guan, Yuanfang |e investigator |4 oth | |
| 700 | 1 | |a Cai, Lingrui |e investigator |4 oth | |
| 700 | 1 | |a Zeng, Kaiman |e investigator |4 oth | |
| 700 | 1 | |a Cragin, Bruce |e investigator |4 oth | |
| 700 | 1 | |a Kaul, Shirya |e investigator |4 oth | |
| 700 | 1 | |a Fowler, Jennifer |e investigator |4 oth | |
| 700 | 1 | |a Tastan, Oznur |e investigator |4 oth | |
| 700 | 1 | |a Kovacevic, Vladimir |e investigator |4 oth | |
| 700 | 1 | |a Alpay, Ege |e investigator |4 oth | |
| 700 | 1 | |a Romanovskii-Chernik, Luiza |e investigator |4 oth | |
| 700 | 1 | |a Romanovskii-Chernik, Aleksandr |e investigator |4 oth | |
| 700 | 1 | |a Bingol, Alper |e investigator |4 oth | |
| 700 | 1 | |a Yılmazer, Sema |e investigator |4 oth | |
| 700 | 1 | |a Yan, Shankai |e investigator |4 oth | |
| 700 | 1 | |a Lin, Santina |e investigator |4 oth | |
| 700 | 1 | |a Arıkan, Ege |e investigator |4 oth | |
| 700 | 1 | |a Varshney, Lav |e investigator |4 oth | |
| 700 | 1 | |a Phuong, Jimmy |e investigator |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t JAMA network open |d 2018 |g 4(2021), 10 vom: 01. Okt., Seite e2124946 |w (DE-627)NLM289300517 |x 2574-3805 |7 nnns |
| 773 | 1 | 8 | |g volume:4 |g year:2021 |g number:10 |g day:01 |g month:10 |g pages:e2124946 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1001/jamanetworkopen.2021.24946 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 4 |j 2021 |e 10 |b 01 |c 10 |h e2124946 | ||