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

Table 5 MetaMap performance for the candidate terms from VetCN dataset

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	84.21	94.12	88.89	95.00	100.00	97.44	94.74	94.74	94.74	95.00	100.00	97.44
arthritis	93.33	73.68	82.35	100.00	75.00	85.71	94.44	89.47	91.89	100.00	90.00	94.74
asthma	100.00	90.00	94.74	100.00	95.00	97.44	89.47	94.44	91.89	100.00	100.00	100.00
ckd	68.75	73.33	70.97	100.00	95.00	97.44	62.50	71.43	66.67	100.00	100.00	100.00
diabetes	76.47	81.25	78.79	100.00	90.00	94.74	88.89	88.89	88.89	94.74	94.74	94.74
epilepsy	100.00	90.00	94.74	100.00	95.00	97.44	100.00	90.00	94.74	100.00	95.00	97.44
glaucoma	87.50	77.78	82.35	94.74	94.74	94.74	93.33	73.68	82.35	94.74	94.74	94.74
heart_failure	73.68	93.33	82.35	95.00	100.00	97.44	84.21	94.12	88.89	100.00	100.00	100.00
hypertension	71.43	62.50	66.67	100.00	95.00	97.44	72.22	86.67	78.79	100.00	100.00	100.00
obesity	75.00	100.00	85.71	85.00	100.00	91.89	84.21	94.12	88.89	89.47	94.44	91.89
osteoarthritis	94.74	94.74	94.74	100.00	95.00	97.44	85.00	100.00	91.89	85.00	100.00	91.89
	84.10	84.61	83.85	97.25	94.07	95.38	86.27	88.87	87.24	96.27	97.17	96.63

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

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