Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development
| Autor: | Jiang, Wei, Kriventsov, Stan, Aktar, Sakifa, Ahamad, Martuza, Rashed-Al-Mahfuz, Azad, AKM, Uddin, Shahadat, Kamal, AHM, Alyami, Salem A, Lin, Ping-I, Islam, Mohammed Shariful, Quinn, Julian MW, Eapen, Valsamma, Moni, Mohammad Ali |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0206 medical engineering
Medical laboratory Decision tree severity morbidity data set Health Informatics 02 engineering and technology lcsh:Computer applications to medicine. Medical informatics Machine learning computer.software_genre 03 medical and health sciences statistical analysis Health Information Management blood Risk of mortality blood samples Medicine Blood test risk 030304 developmental biology Original Paper 0303 health sciences medicine.diagnostic_test business.industry COVID-19 prediction mortality testing Random forest Coronavirus Data set Support vector machine machine learning outcome lcsh:R858-859.7 Artificial intelligence Gradient boosting business computer 020602 bioinformatics |
| Zdroj: | JMIR Medical Informatics, Vol 9, Iss 4, p e25884 (2021) JMIR Medical Informatics |
| ISSN: | 2291-9694 |
| Popis: | Background Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. Conclusions We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|