Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients
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.PMID:38699029 | PMC:PMC11064431 | DOI:10.1016/j.heliyon.2024.e29981
Source: Cancer Control - Category: Cancer & Oncology Authors: Yuchen Fu Xuejing Xu Juan Du Taihong Huang Jiping Shi Guanghao Song Qing Gu Han Shen Sen Wang Source Type: research
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