A Novel AUC Maximization Imbalanced Learning Approach for Predicting Composite Outcomes in COVID-19 Hospitalized Patients

The COVID-19 patient data for composite outcome prediction often comes with class imbalance issues, i.e., only a small group of patients develop severe composite events after hospital admission, while the rest do not. An ideal COVID-19 composite outcome prediction model should possess strong imbalan...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE journal of biomedical and health informatics Ročník 27; číslo 8; s. 3794 - 3805
Hlavní autori: Wang, Guanjin, Kwok, Stephen Wai Hang, Yousufuddin, Mohammed, Sohel, Ferdous
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Area Under Curve
AUC maximization learning
Codes
COVID-19
COVID-19 prediction
Exact solutions
Hospitalization
Hospitals
Humans
imbalance learning
Intubation
Learning
Machine Learning
Maximization
Objective function
Optimization
Patients
Performance measurement
Prediction models
Predictive models
Prognosis
proximal support vector machines
Support vector machines
Training
ISSN:2168-2194, 2168-2208, 2168-2208
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The COVID-19 patient data for composite outcome prediction often comes with class imbalance issues, i.e., only a small group of patients develop severe composite events after hospital admission, while the rest do not. An ideal COVID-19 composite outcome prediction model should possess strong imbalanced learning capability. The model also should have fewer tuning hyperparameters to ensure good usability and exhibit potential for fast incremental learning. Towards this goal, this study proposes a novel imbalanced learning approach called Imbalanced maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM) by the means of classical PSVM to predict the composite outcomes of hospitalized COVID-19 patients within 30 days of hospitalization. ImAUC-PSVM offers the following merits: (1) it incorporates straightforward AUC maximization into the objective function, resulting in fewer parameters to tune. This makes it suitable for handling imbalanced COVID-19 data with a simplified training process. (2) Theoretical derivations reveal that ImAUC-PSVM has the same analytical solution form as PSVM, thus inheriting the advantages of PSVM for handling incremental COVID-19 cases through fast incremental updating. We built and internally and externally validated our proposed classifier using real COVID-19 patient data obtained from three separate sites of Mayo Clinic in the United States. Additionally, we validated it on public datasets using various performance metrics. Experimental results demonstrate that ImAUC-PSVM outperforms other methods in most cases, showcasing its potential to assist clinicians in triaging COVID-19 patients at an early stage in hospital settings, as well as in other prediction applications.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2023.3279824