A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation
| Autor: | Qian Li, Liu Ouyang, Baoli Zhu, Lei Han, Yaorong Ge, Hengdong Zhang, Patrick Robinson, Ming Xu, Shi Chen, Yuanfang Chen, Jie Liu, Forrest Sheng Bao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
China decision support diagnosis MEDLINE Health Informatics Disease 030204 cardiovascular system & hematology Machine learning computer.software_genre lcsh:Computer applications to medicine. Medical informatics Severity of Illness Index Machine Learning 03 medical and health sciences 0302 clinical medicine Severity of illness Medicine Humans 030212 general & internal medicine multimodality reliable clinical type Original Paper Modalities Modality (human–computer interaction) business.industry SARS-CoV-2 lcsh:Public aspects of medicine COVID-19 Reproducibility of Results lcsh:RA1-1270 Gold standard (test) prediction Middle Aged Models Theoretical Triage Random forest classification lcsh:R858-859.7 Female Artificial intelligence business computer |
| Zdroj: | Journal of Medical Internet Research, Vol 23, Iss 4, p e23948 (2021) Journal of Medical Internet Research |
| ISSN: | 1438-8871 |
| Popis: | Background Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority. |
| Databáze: | OpenAIRE |
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