Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.
J Am Med Inform Assoc
; 28(2): 360-364, 2021 02 15.
Article
em En
| MEDLINE
| ID: mdl-33027509
ABSTRACT
OBJECTIVE:
The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. MATERIALS ANDMETHODS:
We used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.RESULTS:
Cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.CONCLUSIONS:
Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Disseminação de Informação
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Conjuntos de Dados como Assunto
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Confiabilidade dos Dados
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Aprendizado de Máquina
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COVID-19
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article