Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies.

BACKGROUND: Mapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components. METHODS: Job title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14 983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in 2 occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix. RESULTS: For 11 991 case-control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6-digit and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (κ 0.6-0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology. CONCLUSIONS: Although some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiological studies.

HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs.

BACKGROUND: Partitioning the human immunoglobulin variable region into variable (V), diversity (D), and joining (J) segments is a common sequence analysis step. We introduce a novel approximate dynamic programming method that uses conserved immunoglobulin gene motifs to improve performance of aligning V-segments of rearranged immunoglobulin (Ig) genes. Our new algorithm enhances the former JOINSOLVER algorithm by processing sequences with insertions and/or deletions (indels) and improves the efficiency for large datasets provided by high throughput sequencing. RESULTS: In our simulations, which include rearrangements with indels, the V-matching success rate improved from 61% for partial alignments of sequences with indels in the original algorithm to over 99% in the approximate algorithm. An improvement in the alignment of human VDJ rearrangements over the initial JOINSOLVER algorithm was also seen when compared to the Stanford.S22 human Ig dataset with an online VDJ partitioning software evaluation tool. CONCLUSIONS: HTJoinSolver can rapidly identify V- and J-segments with indels to high accuracy for mutated sequences when the mutation probability is around 30% and 20% respectively. The D-segment is much harder to fit even at 20% mutation probability. For all segments, the probability of correctly matching V, D, and J increases with our alignment score.

Computer-Based Coding of Occupation Codes for Epidemiological Analyses.

Mapping job titles to standardized occupation classification (SOC) codes is an important step in evaluating changes in health risks over time as measured in inspection databases. However, manual SOC coding is cost prohibitive for very large studies. Computer based SOC coding systems can improve the efficiency of incorporating occupational risk factors into large-scale epidemiological studies. We present a novel method of mapping verbatim job titles to SOC codes using a large table of prior knowledge available in the public domain that included detailed description of the tasks and activities and their synonyms relevant to each SOC code. Job titles are compared to our knowledge base to find the closest matching SOC code. A soft Jaccard index is used to measure the similarity between a previously unseen job title and the knowledge base. Additional information such as standardized industrial codes can be incorporated to improve the SOC code determination by providing additional context to break ties in matches.