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Table 3 Model performance using MILR with smaller training sizes (the highest value for each metric is highlighted in bold type): the effect of formal reports was more obvious when the training size was smaller

From: Adverse event detection by integrating twitter data and VAERS

Twitter data Formal ACC PR RE FS AUC
#Training #Report      
314 (20%) 0 0.7731 0.7278 0.5923 0.6525 0.8446
  500 0.7812 0.7323 0.6212 0.6713 0.8539
  1000 0.8112 0.7993 0.6356 0.7076 0.8888
  1500 0.8136 0.7935 0.6524 0.7151 0.8923
  2000 0.8114 0.7812 0.6612 0.7156 0.8916
  2500 0.8112 0.7824 0.6590 0.7147 0.8904
786 (50%) 0 0.7939 0.7689 0.6141 0.6816 0.8646
  500 0.7920 0.7651 0.6125 0.6790 0.8684
  1000 0.8041 0.7682 0.6567 0.7064 0.8834
  1500 0.8034 0.7720 0.6482 0.7031 0.8834
  2000 0.8092 0.7968 0.6312 0.7044 0.8897
  2500 0.8066 0.7711 0.6615 0.7108 0.8866
1048 (67%) 0 0.7952 0.7841 0.5953 0.6767 0.8646
  500 0.7850 0.7615 0.5915 0.6645 0.8653
  1000 0.7983 0.7948 0.5937 0.6795 0.8843
  1500 0.7996 0.7944 0.5992 0.6830 0.8880
  2000 0.8034 0.7984 0.6080 0.6903 0.8899
  2500 0.8060 0.8016 0.6133 0.6949 0.8910
1179 (75%) 0 0.7952 0.7845 0.5927 0.6752 0.8664
  500 0.7933 0.7695 0.6010 0.6743 0.8846
  1000 0.8034 0.7881 0.6172 0.6915 0.8948
  1500 0.8041 0.7913 0.6154 0.6915 0.8963
  2000 0.8041 0.7940 0.6119 0.6901 0.8983
  2500 0.8041 0.7940 0.6119 0.6901 0.8985