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Table 5 MetaMap performance for the candidate terms from VetCN 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 termCandidate terms (20 top-ranked n-grams) from Skip-gram neural embeddings for a target term
MetaMap Experiment 1MetaMap Experiment 2MetaMap Experiment 1MetaMap Experiment 2
PRFPRFPRFPRF
anaemia84.2194.1288.8995.00100.0097.4494.7494.7494.7495.00100.0097.44
arthritis93.3373.6882.35100.0075.0085.7194.4489.4791.89100.0090.0094.74
asthma100.0090.0094.74100.0095.0097.4489.4794.4491.89100.00100.00100.00
ckd68.7573.3370.97100.0095.0097.4462.5071.4366.67100.00100.00100.00
diabetes76.4781.2578.79100.0090.0094.7488.8988.8988.8994.7494.7494.74
epilepsy100.0090.0094.74100.0095.0097.44100.0090.0094.74100.0095.0097.44
glaucoma87.5077.7882.3594.7494.7494.7493.3373.6882.3594.7494.7494.74
heart_failure73.6893.3382.3595.00100.0097.4484.2194.1288.89100.00100.00100.00
hypertension71.4362.5066.67100.0095.0097.4472.2286.6778.79100.00100.00100.00
obesity75.00100.0085.7185.00100.0091.8984.2194.1288.8989.4794.4491.89
osteoarthritis94.7494.7494.74100.0095.0097.4485.00100.0091.8985.00100.0091.89
 84.1084.6183.8597.2594.0795.3886.2788.8787.2496.2797.1796.63
  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