@文章{info:doi/ 10.2192 /23582，作者=“Luellen, Eric”，标题=“机器学习解释SARS-CoV-2 (COVID-19)的病原-免疫关系，以及预测免疫和治疗机会的模型:一项比较有效性研究”，期刊=“JMIRx Med”，年=“2020”，月=“10”，日=“19”，卷=“1”，数=“1”，页=“e23582”，关键词=“传染病;SARS-CoV-2;COVID-19;公共卫生;免疫力;大规模接种疫苗;治疗;背景:大约80%的COVID-19感染者具有免疫力。他们是无症状的未知携带者，仍然可以感染与他们接触的人。了解是什么使他们免疫，可以为公共卫生政策提供信息，了解谁需要保护以及为什么需要保护，并可能为那些不能或不愿接种疫苗的人提供一种新的治疗方法。 Objective: The primary objectives of this study were to learn if machine learning could identify patterns in the pathogen-host immune relationship that differentiate or predict COVID-19 symptom immunity and, if so, which ones and at what levels. The secondary objective was to learn if machine learning could take such differentiators to build a model that could predict COVID-19 immunity with clinical accuracy. The tertiary purpose was to learn about the relevance of other immune factors. Methods: This was a comparative effectiveness research study on 53 common immunological factors using machine learning on clinical data from 74 similarly grouped Chinese COVID-19--positive patients, 37 of whom were symptomatic and 37 asymptomatic. The setting was a single-center primary care hospital in the Wanzhou District of China. Immunological factors were measured in patients who were diagnosed as SARS-CoV-2 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) in the 14 days before observations were recorded. The median age of the 37 asymptomatic patients was 41 years (range 8-75 years); 22 were female, 15 were male. For comparison, 37 RT-PCR test--positive patients were selected and matched to the asymptomatic group by age, comorbidities, and sex. Machine learning models were trained and compared to understand the pathogen-immune relationship and predict who was immune to COVID-19 and why, using the statistical programming language R. Results: When stem cell growth factor-beta (SCGF-$\beta$) was included in the machine learning analysis, a decision tree and extreme gradient boosting algorithms classified and predicted COVID-19 symptom immunity with 100{\%} accuracy. When SCGF-$\beta$ was excluded, a random-forest algorithm classified and predicted asymptomatic and symptomatic cases of COVID-19 with 94.8{\%} AUROC (area under the receiver operating characteristic) curve accuracy (95{\%} CI 90.17{\%}-100{\%}). In total, 34 common immune factors have statistically significant associations with COVID-19 symptoms (all c<.05), and 19 immune factors appear to have no statistically significant association. Conclusions: The primary outcome was that asymptomatic patients with COVID-19 could be identified by three distinct immunological factors and levels: SCGF-$\beta$ (>127,637), interleukin-16 (IL-16) (>45), and macrophage colony-stimulating factor (M-CSF) (>57). The secondary study outcome was the suggestion that stem-cell therapy with SCGF-$\beta$ may be a novel treatment for COVID-19. Individuals with an SCGF-$\beta$ level >127,637, or an IL-16 level >45 and an M-CSF level >57, appear to be predictively immune to COVID-19 100{\%} and 94.8{\%} (AUROC) of the time, respectively. Testing levels of these three immunological factors may be a valuable tool at the point of care for managing and preventing outbreaks. Further, stem-cell therapy via SCGF-$\beta$ and M-CSF appear to be promising novel therapeutics for patients with COVID-19. ", issn="2563-6316", doi="10.2196/23582", url="https://med.jmirx.org/2020/1/e23582/", url="https://doi.org/10.2196/23582" }