A system for automated lexical mapping

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.

Figure 1.

An overall flowchart detailing the steps of the alignment process.

Figure 2.

Matrix representation of alignment. Query term is alk ptase (on the vertical) and dictionary term is alkaline phosphatase (on the horizontal). Highlighted cells represent positions where a character of the query term matches a character from the dictionary term within the optimal alignment.

Figure 3.

A graphical representation of how the chain is linked together. The dark solid arrows of the matrix show the reading of the cells from lower left and up the column. Each column is read from bottom to top, proceeding from the leftmost column to the right. The lower image shows the chain representation of the matrix. Coordinates represent the position of the cell in the matrix. The cells marked “1” are read from the matrix starting from the lower left corner, up the first column (column 0 in ▶), then proceeding up the second column (column 1) and continuing through the table from left to right.