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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