Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients

Yuchen Fu; Xuejing Xu; Juan Du; Taihong Huang; Jiping Shi; Guanghao Song; Qing Gu; Han Shen; Sen Wang

doi:10.1016/j.heliyon.2024.e29981

Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients

Heliyon. 2024 Apr 21;10(9):e29981. doi: 10.1016/j.heliyon.2024.e29981. eCollection 2024 May 15.

Authors

Yuchen Fu^{1

2}, Xuejing Xu¹, Juan Du³, Taihong Huang¹, Jiping Shi¹, Guanghao Song¹, Qing Gu², Han Shen¹, Sen Wang¹

Affiliations

¹ Department of Clinical Laboratory Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210008, China.
² State Key Laboratory for Novel Software Technology, National Institute of Healthcare Data Science at Nanjing University, Nanjing, 210008, China.
³ Comprehensive Cancer Center of Drum Tower Hospital, Medical School of Nanjing University, Clinical Cancer Institute of Nanjing University, Nanjing, 210008, China.

Abstract

Amidst the global COVID-19 pandemic, the urgent need for timely and precise patient prognosis assessment underscores the significance of leveraging machine learning techniques. In this study, we present a novel predictive model centered on routine clinical laboratory test data to swiftly forecast patient survival outcomes upon admission. Our model integrates feature selection algorithms and binary classification algorithms, optimizing algorithmic selection through meticulous parameter control. Notably, we developed an algorithm coupling Lasso and SVM methodologies, achieving a remarkable area under the ROC curve of 0.9277 with the use of merely 8 clinical laboratory parameters collected upon admission. Our primary contribution lies in the utilization of straightforward laboratory parameters for prognostication, circumventing data processing intricacies, and furnishing clinicians with an expeditious and precise prognostic assessment tool.

Keywords: COVID-19; Lasso; Machine learning; Prognostic prediction; SVM.