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Table 1 Clinical characteristics (n = 348) and performance statistics. Age and lab values are shown as mean ± std.

From: Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning

  Clinical variable Severe-late Severe-early Non-severe
Basic patient information # of patients 71 71 206
  Male (%) 49.3 49.3 50.0
  Age (years) 62.6 ± 13.9 69.8 ± 12.9 60.3 ± 16.9
Labs Absolute Lymphocyte Counts (K/mcL) 3.57 ± 17.04 1.30 ± 2.02 2.45 ± 14.00
  D-Dimer (mcg/mL) 6.58 ± 7.26 3.52 ± 2.90 2.16 ± 1.75
  Interleukin 6 (pg/mL) 469.60 ± 816.95 116.50 ± 90.25 62.62 ± 47.35
  Ferritin (ng/mL) 1016.36 ± 1045.78 983.09 ± 1279.56 467.76 ± 541.13
  Platelets (K/mcL) 171.31 ± 114.04 196.04 ± 112.71 218.33 ± 118.85
Cancer-related Lymphoma (%) 11.3 5.6 1.9
  Lung (%) 5.6 21.1 6.8
  Leukemia (%) 19.7 8.5 5.3
Diagnosis (ICD) I1 - Hypertensive diseases (%) 52.1 71.8 56.8
  I4 - Cardiac disorders (%) 31.0 35.2 26.2
  J4 - Chronic lower respiratory disease (%) 19.7 28.2 17.5
Radiology Retic. Opacities (%) 16.9 29.6 14.6
  Effusions (%) 12.7 15.5 7.8
  Airspace Opacity (%) 32.4 81.7 38.3
Performance AUROC (Our method) 0.704 0.829 0.710
  AUROC (Yan et al.) 0.456 0.634 0.499
  AUROC (Huang et al.) 0.600 0.638 0.604
  Avg. Precision (Our method) 0.366 0.578 0.772
  Avg. Precision (Yan et al.) 0.190 0.315 0.588
  Avg. Precision (Huang et al.) 0.291 0.326 0.691