Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19.
Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19.
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DOI：
10.1016/j.eswa.2021.114832
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发表时间：
2021-08-15
影响因子：
8.5
通讯作者：
Alvarez F
中科院分区：
文献类型：
--
作者：
Smith M;Alvarez F
在本文中，我们将一系列机器学习模型应用于最近发布的有关 COVID19 患者死亡率的独特数据集。我们使用的数据集包含中国武汉地区一家医院收治的 375 名患者的血液样本。有201名患者在住院期间幸存，174名患者在医院期间死亡。本文的重点不仅在于了解哪种机器学习模型能够获得绝对最高的准确度，还在于对机器学习模型提供的内容进行解释。我们发现，在预测患者死亡率时，年龄、住院天数、淋巴细胞和中性粒细胞是重要且可靠的预测因素。此外，我们使用的算法使我们能够观察每个变量对具体患者水平的边际影响，这可能有助于从业者轻松检测异常模式。本文分析了机器学习模型对新冠肺炎患者的全局和局部解释。
In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.
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DOI：
10.1007/s10044-021-00984-y
发表时间：
2021
期刊：
Pattern analysis and applications : PAA
影响因子：
--
作者：
Narin A;Kaya C;Pamuk Z
通讯作者：
Pamuk Z
影响因子：
4.6
作者：
Wang L;Lin ZQ;Wong A
通讯作者：
Wong A
影响因子：
7.8
作者：
Singh, Sarbjit;Parmar, Kulwinder Singh;Kumar, Jatinder
通讯作者：
Kumar, Jatinder
影响因子：
168.9
作者：
Chen, Huijun;Guo, Juanjuan;Zhang, Yuanzhen
通讯作者：
Zhang, Yuanzhen
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