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Table 2 Classifier performance (recall, precision and F-score). SVM RBF with binary count feature extraction was the most effective combination to identify incident type and severity level

From: Using multiclass classification to automate the identification of patient safety incident reports by type and severity

  Benchmark Original Independent
  Recall Precision F-score Recall Precision F-score Recall Precision F-score
Incident type a 78.3 78.3 78.3 73.9 73.9 73.9 68.5 68.5 68.5
Falls 96.2 83.3 89.3 95.6 96.6 96.1 91.3 86.5 88.8
Medications 76.9 76.9 76.9 80.9 91.7 85.9 81.1 78.6 79.8
Pressure injury 88.5 100.0 93.9 89.2 86.8 88.0 96.8 76.0 85.2
Aggression 92.3 88.9 90.6 81.6 76.9 79.2 81.5 62.2 70.6
Documentation 46.2 63.2 53.3 46.2 31.6 37.5 47.6 16.0 24.0
Blood products 80.8 95.5 87.5 100.0 62.5 76.9 83.1 43.0 56.6
Patient identification 84.6 61.1 71.0 71.4 25.0 37.0 23.3 44.4 30.5
Infection 92.3 88.9 90.6 83.3 38.5 52.6 40.9 13.2 20.0
Clinical handover 80.8 65.6 72.4 71.4 18.5 29.4 37.9 14.3 20.8
Deteriorating patient 92.3 85.7 88.9 100.0 25.0 40.0 21.4 17.6 19.4
Others 30.8 50.0 38.1 54.7 85.3 66.7 57.1 87.0 69.0
SAC level a 62.9 62.9 62.9 50.1 50.1 50.1 52.7 52.7 52.7
SAC1 82.8 92.3 87.3 84.0 11.2 19.8 82.6 6.8 12.5
SAC2 41.4 60.0 49.0 43.2 7.2 12.3 16.2 9.6 12.0
SAC3 44.8 54.2 49.1 35.9 52.3 42.6 46.9 49.8 48.3
SAC4 82.8 52.2 64.0 62.4 61.2 61.8 58.3 61.8 60.0
  1. aMicro-averaging measures