Early and fair COVID-19 outcome risk assessment using robust feature selection

• Published in: Scientific reports 2023-11, Vol.13 (1), p.18981-18981, Article 18981
• Main Authors: Giuste, Felipe O., He, Lawrence, Lais, Peter, Shi, Wenqi, Zhu, Yuanda, Hornback, Andrew, Tsai, Chiche, Isgut, Monica, Anderson, Blake, Wang, May D.
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2413; 692/499; 692/53/2423; Automation; COVID-19; Dementia disorders; Demography; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Patients; Precision medicine; Preventable deaths; Risk assessment; Science; Science (multidisciplinary); Ventilators
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-36175-4

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.