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Table 6 MetaMap performance for the candidate terms from PMSB dataset

From: Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes

Target term (n-gram)

Candidate terms (20 top-ranked n-grams) from CBOW neural embeddings for a target term

Candidate terms (20 top-ranked n-grams) from Skip-gram neural embeddings for a target term

MetaMap Experiment 1

MetaMap Experiment 2

MetaMap Experiment 1

MetaMap Experiment 2

P

R

F

P

R

F

P

R

F

P

R

F

anaemia

90.00

100.00

94.74

85.00

100.00

91.89

84.21

94.12

88.89

94.74

94.74

94.74

arthritis

88.89

88.89

88.89

89.47

94.44

91.89

100.00

100.00

100.00

95.00

100.00

97.44

asthma

76.47

81.25

78.79

72.22

86.67

78.79

63.16

92.31

75.00

68.42

92.86

78.79

CKD

100.00

100.00

100.00

100.00

100.00

100.00

90.00

100.00

94.74

90.00

100.00

94.74

diabetes

63.16

92.31

75.00

68.42

92.86

78.79

75.00

100.00

85.71

80.00

100.00

88.89

epilepsy

85.00

100.00

91.89

95.00

100.00

97.44

90.00

100.00

94.74

95.00

100.00

97.44

glaucoma

90.00

100.00

94.74

100.00

100.00

100.00

84.21

94.12

88.89

100.00

100.00

100.00

heart_failure

85.00

100.00

91.89

90.00

100.00

94.74

73.68

93.33

82.35

90.00

100.00

94.74

hypertension

95.00

100.00

97.44

100.00

100.00

100.00

84.21

94.12

88.89

95.00

100.00

97.44

obesity

100.00

95.00

97.44

100.00

100.00

100.00

94.74

94.74

94.74

95.00

100.00

97.44

osteoarthritis

90.00

100.00

94.74

100.00

100.00

100.00

90.00

100.00

94.74

100.00

100.00

100.00

 

87.59

96.13

91.41

90.92

97.63

93.96

84.47

96.61

89.88

91.20

98.87

94.70

  1. The table shows the performance of MetaMap in Experiment 1 (applying MetaMap to the candidate terms) and Experiment 2 (short form detection and expansion into long form before applying MetaMap to the candidate terms) for each target term (n-gram for a well-known medical condition). The candidate terms are a list of the 20 top-ranked terms (highest cosine value) obtained from the created neural embeddings with CBOW or Skip-gram taking the vector for a target term. The last row shows the average of each evaluation measure over all 11 medical conditions under study to get an overall measure of performance (a.k.a. macro-averaging). Abbreviations: P = precision; R = recall; and F = F measure