Table 1 A list of variables and their corresponding category utilized in predicting COVID-19 readmission risk
From: Predictive modeling for COVID-19 readmission risk using machine learning algorithms
Type | Category | Variables |
|---|---|---|
Inputs | Demographic characteristics | Age, sex, height, weight, blood group, hospitalization length of stay (LOS) |
Clinical manifestation | Dry cough, nausea, headache, gastrointestinal (GI) manifestation, Chill, loss of taste and smell, rhinorrhea, sore throat, contusion, high body temperature, muscular pain, vomiting, dyspnea | |
Past medical history and comorbidities | Cardiac disease, smoking, pneumonia, hypertension (diastolic/ systolic), alcohol addiction, diabetes, and other underline diseases | |
Laboratory results | Red-cell count, hematocrit, hemoglobin, absolute lymphocyte count, blood calcium, blood potassium, absolute neutrophil count, alanine aminotransferase (ALT), magnesium, prothrombin time, alkaline phosphatase, platelet count, hypersensitive troponin creatinine, white cell count, aspartate aminotransferase (ASP), blood glucose, total bilirubin, erythrocyte sedimentation rate (ESR), C-reactive protein(CRP), albumin, thromboplastin time, lactate dehydrogenase (LDH), D-dimer, blood phosphorus, blood sodium, and blood urea nitrogen (BUN), oxygen saturation | |
Radiological factors | Pleural fluid, consolidation | |
Treatment | Oxygen therapy | |
Output | Readmission: yes (1), no (0) | |