Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning.
Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning.
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DOI：
10.3389/frai.2021.673527
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发表时间：
2021
影响因子：
4
通讯作者：
Mansouri N
中科院分区：
文献类型：
--
作者：
Jamshidi E;Asgary A;Tavakoli N;Zali A;Dastan F;Daaee A;Badakhshan M;Esmaily H;Jamaldini SH;Safari S;Bastanhagh E;Maher A;Babajani A;Mehrazi M;Sendani Kashi MA;Jamshidi M;Sendani MH;Rahi SJ;Mansouri N
背景：早期预测COVID-19患者的症状和死亡风险将改善医疗结果，合理分配医疗资源，降低医疗成本，有助于制定疫苗优先级和自我隔离策略，从而降低疾病的患病率。然而，缺乏这种可公开访问的预测模型。方法：在综合评估现有机器学习（ML）方法的基础上，仅基于2020年2月至9月23,749例医院确诊的COVID-19患者的年龄、性别和病史，建立了两个模型：症状预测模型（SPM）和死亡率预测模型（MPM）。SPM预测每个病人有12个症状组：呼吸窘迫、意识障碍、胸痛、麻痹或麻痹、咳嗽、发烧或发冷、胃肠道症状、喉咙痛、头痛、眩晕、嗅觉或味觉丧失、肌肉疼痛或疲劳。MPM预测covid -19阳性个体的死亡。结果：SPM对症状的roc - auc为0.53 ~ 0.78。最准确的预测是意识障碍，灵敏度为74%，特异性为70%。在研究人群中观察到2440例死亡。MPM的ROC-AUC为0.79，预测死亡率的敏感性为75%，特异性为70%。大约90%的死亡发生在21%的高危人群中。为了让患者和临床医生能够轻松地使用这些模型，我们在www.aicovid.net创建了一个免费访问的在线界面。结论：ML模型使用患者和临床医生随时可用的信息预测covid -19相关症状和死亡率。因此，两者都可以快速估计疾病的严重程度，从而在未来几个月内就住院治疗、自我隔离策略和COVID-19疫苗优先级做出更好的共同医疗保健决策。
Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53–0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.
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影响因子：
2.9
作者：
Fumagalli C;Rozzini R;Vannini M;Coccia F;Cesaroni G;Mazzeo F;Cola M;Bartoloni A;Fontanari P;Lavorini F;Marcucci R;Morettini A;Nozzoli C;Peris A;Pieralli F;Pini R;Poggesi L;Ungar A;Fumagalli S;Marchionni N
通讯作者：
Marchionni N
影响因子：
56.3
作者：
Tabata, Sakiko;Imai, Kazuo;Tamura, Kaku
通讯作者：
Tamura, Kaku
DOI：
10.1016/s2589-7500(20)30217-x
发表时间：
2020-10
期刊：
The Lancet. Digital health
影响因子：
--
作者：
Yadaw AS;Li YC;Bose S;Iyengar R;Bunyavanich S;Pandey G
通讯作者：
Pandey G
影响因子：
20.4
作者：
Mandrekar, Jayawant N.
通讯作者：
Mandrekar, Jayawant N.
影响因子：
4.8
作者：
Jamshidi E;Babajani A;Soltani P;Niknejad H
通讯作者：
Niknejad H