Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality In Post COVID-19 Era.
IEEE Rev Biomed Eng
; PP2022 Oct 21.
Artigo
em Inglês
| MEDLINE | ID: covidwho-2231267
ABSTRACT
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Tipo de estudo:
Estudo observacional
/
Estudo prognóstico
/
Revisões
/
Revisão sistemática/Meta-análise
Tópicos:
Covid persistente
Idioma:
Inglês
Ano de publicação:
2022
Tipo de documento:
Artigo
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