Generalizable prediction of COVID-19 mortality on worldwide patient data.
Generalizable prediction of COVID-19 mortality on worldwide patient data.
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基于全球患者数据的新冠肺炎死亡率概括性预测。
DOI：
10.1093/jamiaopen/ooac036
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发表时间：
2022-07
期刊：
影响因子：
2.1
通讯作者：
Kuo, Tsung-Ting
中科院分区：
文献类型：
--
作者：
Edelson, Maxim;Kuo, Tsung-Ting
预测2019冠状病毒病（COVID-19）患者的死亡率对于早期护理和干预至关重要。现有的研究主要是在地理范围或规模有限的数据集上建立模型。在这项研究中，我们在全球范围内，大规模的“稀疏”数据和数据的“密集”子集上开发了COVID-19死亡率预测模型。我们评估了6个分类器，包括逻辑回归（LR），支持向量机（SVM），随机森林（RF），多层感知器（MLP），AdaBoost（AB）和朴素贝叶斯（NB）。我们还进行了时间分析，并使用Isotonic Regression校准了我们的模型。结果表明，AB在稀疏数据集上的表现优于其他分类器，而LR在密集数据集上的表现最好（接收器工作特征曲线下的面积，或稀疏数据集的AUC = 0.7，密集数据集的AUC = 0.963）。我们还确定了有影响力的特征，如症状、国家、年龄和死亡/出院日期。我们的所有模型都是良好校准的（P > .1）。我们的研究结果突出了使用稀疏训练数据来提高泛化能力与在密集数据上训练的权衡，后者产生更高的区分结果。我们发现，协变量，如患者的症状信息，国家（报告病例的国家），年龄和出院或死亡日期是死亡率预测的最重要因素。这项研究是在COVID-19时代和潜在的其他大流行病期间提高医疗质量的垫脚石。我们的代码可在https://doi.org/10.5281/zenodo.6336231上公开获取。
Predicting Coronavirus disease 2019 (COVID-19) mortality for patients is critical for early-stage care and intervention. Existing studies mainly built models on datasets with limited geographical range or size. In this study, we developed COVID-19 mortality prediction models on worldwide, large-scale “sparse” data and on a “dense” subset of the data. We evaluated 6 classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), AdaBoost (AB), and Naive Bayes (NB). We also conducted temporal analysis and calibrated our models using Isotonic Regression. The results showed that AB outperformed the other classifiers for the sparse dataset, while LR provided the highest-performing results for the dense dataset (with area under the receiver operating characteristic curve, or AUC ≈ 0.7 for the sparse dataset and AUC = 0.963 for the dense one). We also identified impactful features such as symptoms, countries, age, and the date of death/discharge. All our models are well-calibrated (P > .1). Our results highlight the tradeoff of using sparse training data to increase generalizability versus training on denser data, which produces higher discrimination results. We found that covariates such as patient information on symptoms, countries (where the case was reported), age, and the date of discharge from the hospital or death were the most important for mortality prediction. This study is a stepping-stone towards improving healthcare quality during the COVID-19 era and potentially other pandemics. Our code is publicly available at: https://doi.org/10.5281/zenodo.6336231.
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影响因子：
24
作者：
Broberg CS;Kovacs AH;Sadeghi S;Rosenbaum MS;Lewis MJ;Carazo MR;Rodriguez FH 3rd;Halpern DG;Feinberg J;Galilea FA;Baraona F;Cedars AM;Ko JM;Porayette P;Maldonado J;Sarubbi B;Fusco F;Frogoudaki AA;Nir A;Chaudhry A;John AS;Karbassi A;Hoskoppal AK;Frischhertz BP;Hendrickson B;Bouma BJ;Rodriguez-Monserrate CP;Broda CR;Tobler D;Gregg D;Martinez-Quintana E;Yeung E;Krieger EV;Ruperti-Repilado FJ;Giannakoulas G;Lui GK;Ephrem G;Singh HS;Almeneisi HM;Bartlett HL;Lindsay I;Grewal J;Nicolarsen J;Araujo JJ;Cramer JW;Bouchardy J;Al Najashi K;Ryan K;Alshawabkeh L;Andrade L;Ladouceur M;Schwerzmann M;Greutmann M;Meras P;Ferrero P;Dehghani P;Tung PP;Garcia-Orta R;Tompkins RO;Gendi SM;Cohen S;Klewer S;Hascoet S;Mohammadzadeh S;Upadhyay S;Fisher SD;Cook S;Cotts TB;Aboulhosn JA
通讯作者：
Aboulhosn JA
DOI：
10.1016/j.smhl.2020.100178
发表时间：
2021-04
期刊：
Smart health (Amsterdam, Netherlands)
影响因子：
--
作者：
Pourhomayoun M;Shakibi M
通讯作者：
Shakibi M
影响因子：
4
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
通讯作者：
Mansouri N
影响因子：
5.1
作者：
Albitar, Orwa;Ballouze, Rama;Ghadzi, Siti Maisharah Sheikh
通讯作者：
Ghadzi, Siti Maisharah Sheikh
DOI：
10.1016/j.numecd.2020.07.031
发表时间：
2020-10-30
期刊：
Nutrition, metabolism, and cardiovascular diseases : NMCD
影响因子：
--
作者：
Di Castelnuovo A;Bonaccio M;Costanzo S;Gialluisi A;Antinori A;Berselli N;Blandi L;Bruno R;Cauda R;Guaraldi G;My I;Menicanti L;Parruti G;Patti G;Perlini S;Santilli F;Signorelli C;Stefanini GG;Vergori A;Abdeddaim A;Ageno W;Agodi A;Agostoni P;Aiello L;Al Moghazi S;Aucella F;Barbieri G;Bartoloni A;Bologna C;Bonfanti P;Brancati S;Cacciatore F;Caiano L;Cannata F;Carrozzi L;Cascio A;Cingolani A;Cipollone F;Colomba C;Crisetti A;Crosta F;Danzi GB;D'Ardes D;de Gaetano Donati K;Di Gennaro F;Di Palma G;Di Tano G;Fantoni M;Filippini T;Fioretto P;Fusco FM;Gentile I;Grisafi L;Guarnieri G;Landi F;Larizza G;Leone A;Maccagni G;Maccarella S;Mapelli M;Maragna R;Marcucci R;Maresca G;Marotta C;Marra L;Mastroianni F;Mengozzi A;Menichetti F;Milic J;Murri R;Montineri A;Mussinelli R;Mussini C;Musso M;Odone A;Olivieri M;Pasi E;Petri F;Pinchera B;Pivato CA;Pizzi R;Poletti V;Raffaelli F;Ravaglia C;Righetti G;Rognoni A;Rossato M;Rossi M;Sabena A;Salinaro F;Sangiovanni V;Sanrocco C;Scarafino A;Scorzolini L;Sgariglia R;Simeone PG;Spinoni E;Torti C;Trecarichi EM;Vezzani F;Veronesi G;Vettor R;Vianello A;Vinceti M;De Caterina R;Iacoviello L;COvid-19 RISk and Treatments (CORIST) collaboration
通讯作者：
COvid-19 RISk and Treatments (CORIST) collaboration