Nt straight more than every UMLS term. This can also be explained

Therefore, along with querying the original names, we also query the names with 1, 2, three, and 4 HA15 biological activity Character variations. To complement the above test groups, we make use of the following group to test how efficient the query algorithm handles short terms which might be queried generally in genuine predicament. Group 3: We randomly picked 100 healthcare concepts with 5-31 characters. Since several of these concepts are quite brief, we only apply 1 and 2 random character variations, which includes (1) deleting a character, (two) replacing a character, (3) merging two words.Nt straight more than each and every UMLS term. This can also be explained by our complexity analysis above. When t=1 (t would be the number of words in a query), LCS complexity is O(d2M) whilst the LDPMap is O(d2K+M). Considering the fact that Ktitle= SART.S23503 unique characters. The two groups are for two different testing purposes. Group 1: We'll use group 1 to test how successful the query workflow handles pure English name terms, and English name terms with input errors, variations, and typos. Hence, along with querying the original names, we also query the names with 1, two, 3, and four character variations. Character variations are generated randomly within this study, which includes (1) deleting a character, (2) replacinga character, (three) merging two words, i.e., deleting the white space among two words. Group 2: We are going to use group two to test how powerful the query algorithm is in handling a lot of expert medical terms, which may possibly include a great number of special characters, including chemical compounds and drugs.