Earticle

초록

영어

Background: As preexisting comorbidities are risk factors for Coronavirus Disease 19 (COVID-19), improved tools are needed for screening or diagnosing COVID-19 in clinical practice. Difficulties of including vulnerable patient data may create data imbalance and hinder the provision of well-performing prediction tools, such as artificial intelligence (AI) models. Thus, we systematically reviewed studies on AI prognosis prediction in patients infected with COVID-19 and existing comorbidities, including cancer, to investigate model performance and predictors dependent on patient data. PubMed and Cochrane Library databases were searched. This study included research meeting the criteria of using AI to predict outcomes in COVID-19 patients, whether they had cancer or not. Preprints, abstracts, reviews, and animal studies were excluded from the analysis. Majority of non-cancer studies (54.55 percent) showed an area under the curve (AUC) of >0.90 for AI models, whereas 30.77 percent of cancer studies showed the same result. For predicting mortality (3.85 percent), severity (8.33 percent), and hospitalization (14.29 percent), only cancer studies showed AUC values between 0.50 and 0.69. The distribution of comorbidity data varied more in non-cancer studies than in cancer studies but age was indicated as the primary predictor in all studies. Non-cancer studies with more balanced datasets of comorbidities showed higher AUC values than cancer studies. Based on the current findings, dataset balancing is essential for improving AI performance in predicting COVID-19 in patients with comorbidities, especially considering age.

목차

ABSTRACT
Materials and Methods
Data sources and search strategy
Study selection
Data extraction
Data synthesis
Results
Study Selection
Study Description
Performance metrics of AI models in cancer and non-cancerstudies
Important predictors comparing datasets with cancer to without cancer infected with COVID-19
Discussion
Conclusion
Conflict of Interest
References

키워드

저자

• Takdon Kim [ Clinical Trials Center, Chungnam National University Hospital, Daejeon 35015, Republic of Korea ]
• Heeyoung Lee [ College of Pharmacy, Inje University, Inje Institute of Pharmaceutical Sciences and Research, Inje University, Gimhae 50834, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국임상약학회지 [Korean Journal of Clinical Pharmacy]

• 간기

  계간

• pISSN

  1226-6051

• 수록기간

  1991~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 518 DDC 615