Jennifer Y Sun1, Yao Sun. 1. Newborn Medicine Informatics Program, Children's Hospital, Boston, MA, USA. jennifer.sun@childrens.harvard.edu
Abstract
OBJECTIVE: To automate the mapping of disparate databases to standardized medical vocabularies. BACKGROUND: Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process whereby vocabularies are compared and similar concepts are mapped. DESIGN: Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as the UMLS (Unified Medical Language System) and LOINC (Logical Observation Identifier Names and Codes). MEASUREMENTS: This automated lexical mapping was evaluated using three real-world laboratory databases from different health care institutions. The authors report the sensitivity, specificity, percentage correct (true positives plus true negatives divided by total number of terms), and true positive and true negative rates as measures of system performance. RESULTS: The alignment algorithm was able to map 57% to 78% (average of 63% over all runs and databases) of equivalent concepts through lexical mapping alone. True positive rates ranged from 18% to 70%; true negative rates ranged from 5% to 52%. CONCLUSION: Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases.
OBJECTIVE: To automate the mapping of disparate databases to standardized medical vocabularies. BACKGROUND: Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process whereby vocabularies are compared and similar concepts are mapped. DESIGN: Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as the UMLS (Unified Medical Language System) and LOINC (Logical Observation Identifier Names and Codes). MEASUREMENTS: This automated lexical mapping was evaluated using three real-world laboratory databases from different health care institutions. The authors report the sensitivity, specificity, percentage correct (true positives plus true negatives divided by total number of terms), and true positive and true negative rates as measures of system performance. RESULTS: The alignment algorithm was able to map 57% to 78% (average of 63% over all runs and databases) of equivalent concepts through lexical mapping alone. True positive rates ranged from 18% to 70%; true negative rates ranged from 5% to 52%. CONCLUSION: Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases.
Authors: Omar Bouhaddou; Pradnya Warnekar; Fola Parrish; Nhan Do; Jack Mandel; John Kilbourne; Michael J Lincoln Journal: J Am Med Inform Assoc Date: 2007-12-20 Impact factor: 4.497