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Table 1 Text classifiers were trained to identify reports about 10 safety problems in hospitals by type and severity level. This table shows the composition of balanced and stratified datasets used for classifier training and testing

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

  balanced AIMS benchmark stratified AIMS original stratified Riskman independent
n n % n %
Incident type      
 Falls 260 90 20 872 15
 Medications 260 68 15 1053 18
 Pressure injury 260 37 8 190 3
 Aggression 260 49 11 487 8
 Documentation 260 26 6 252 4
 Blood product 260 5 1 59 1
 Patient identification 260 7 2 86 1
 Infection 260 6 1 22 <1
 Clinical handover 260 7 2 87 1
 Deteriorating patient 260 1 <1 14 <1
 Others 260 148 33 2878 48
Total 2860 444   6000  
Severity level      
 SAC1 290 25 <1 23 <1
 SAC2 290 95 2 105 2
 SAC3 290 2198 45 2609 44
 SAC4 290 2519 52 3213 54
Total 1160 4837   5950