Demystifying COVID-19 Mortality Causes with Interpretable Data Mining

Abstract

While SARS-CoV-2 infection rates are declining, older adults remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, such as bilirubin, organ failure, patient age, and underlying disease, they fail to provide a comprehensive analysis to reveal their relationships and interactive effects on the risk of death. Based on the demographic information, inspection indicators, and underlying diseases of 1917 patients (102 were dead) admitted to Xiangya Hospital over a 4-month period, we used the association rule mining method to identify the risk factors leading causes of death among elderly Omicron patients. Firstly, we used the Affinity Propagation clustering to extract key features such as blood parameters, liver function indicators, renal function indicators, coagulation function indicators, and underlying diseases affecting death from the dataset. Then, we applied the Apriori to obtain 7 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. For instance, patients with “C-reactive protein > 8 mg/L”, “neutrophils percentage > 75.0 %”, “lymphocytes percentage < 20 %”, and “albumin < 40 g/L” have a 2x mortality rate than the basic one. If the characteristics of “D-dimer > 0.5 mg/L” and “WBC > 9.5 * 10 9 /L” are continuously included in this foundation, the mortality rate can be increased to 3x or 4x. In addition, we also found that liver and kidney diseases significantly affect patient mortality. Given patients with liver and renal diseases associated with other abnormal features, their mortality rate can be as high as 100 %. These findings can support auxiliary diagnosis and treatment to, facilitate early intervention in patients, thereby reducing patient mortality.