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

ABSTRACT: 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 (? 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.

SUBMITTER: Russ DE

PROVIDER: S-EPMC4871757 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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

Russ Daniel E DE Ho Kwan-Yuet KY Colt Joanne S JS Armenti Karla R KR Baris Dalsu D Chow Wong-Ho WH Davis Faith F Johnson Alison A Purdue Mark P MP Karagas Margaret R MR Schwartz Kendra K Schwenn Molly M Silverman Debra T DT Johnson Calvin A CA Friesen Melissa C MC

Occupational and environmental medicine 20160421 6

<h4>Background</h4>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.<h4>Methods</h4>Job title and task-based classifiers were d ...[more]

PMID: 27102331

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Project description:ObjectivesComputer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a computerized algorithm designed to code free-text job descriptions to US SOC-2010 system based on free-text job titles and work tasks, to evaluate its accuracy.MethodsSOCcer v2 was updated by expanding the training data to include jobs from several epidemiologic studies and revising the algorithm to account for nonlinearity and incorporate interactions. We evaluated the agreement between codes assigned by experts and the highest scoring code (a measure of confidence in the algorithm-predicted assignment) from SOCcer v1 and v2 in 14,714 jobs from three epidemiology studies. We also linked exposure estimates for 258 agents in the job-exposure matrix CANJEM to the expert and SOCcer v2-assigned codes and compared those estimates using kappa and intraclass correlation coefficients. Analyses were stratified by SOCcer score, score distance between the top two scoring codes from SOCcer, and features from CANJEM.ResultsSOCcer's v2 agreement at the 6-digit level was 50%, compared to 44% in v1, and was similar for the three studies (38%-45%). Overall agreement for v2 at the 2-, 3-, and 5-digit was 73%, 63%, and 56%, respectively. For v2, median ICCs for the probability and intensity metrics were 0.67 (IQR 0.59-0.74) and 0.56 (IQR 0.50-0.60), respectively. The agreement between the expert and SOCcer assigned codes linearly increased with SOCcer score. The agreement also improved when the top two scoring codes had larger differences in score.ConclusionsOverall agreement with SOCcer v2 applied to job descriptions from North American epidemiologic studies was similar to the agreement usually observed between two experts. SOCcer's score predicted agreement with experts and can be used to prioritize jobs for expert review.

Project description:Discrimination in the workplace is illegal, yet discriminatory practices remain a persistent global problem. To identify discriminatory practices in the workplace, job advertisement analysis was used by previous studies. However, most of those studies adopted content analysis by manually coding the text from a limited number of samples since working with a large scale of job advertisements consisting of unstructured text data is very challenging. Encountering those limitations, the present study involves text mining techniques to identify multiple types of direct discrimination on a large scale of online job advertisements by designing a method called Direct Discrimination Detection (DDD). The DDD is constructed using a combination of N-grams and regular expressions (regex) with the exact match principle of a Boolean retrieval model. A total of 8,969 online job advertisements in English and Bahasa Indonesia, published from May 2005 to December 2017 were collected from bursakerja-jateng.com as the data. The results reveal that the practices of direct discrimination still exist during the job-hunting process including gender, marital status, physical appearances, and religion. The most recurrent type of discrimination which occurs in job advertisements is based on age (66.27%), followed by gender (38.76%), and physical appearances (18.42%). Additionally, female job seekers are found as the most vulnerable party to experience direct discrimination during recruitment. The results exhibit female job seekers face complex jeopardy in particular job positions comparing to their male counterparts. Not only excluded because of their gender, but female job seekers also had to fulfil more requirements for getting an opportunity to apply for the jobs such as being single, still at a young age, complying specific physical appearances and particular religious preferences. This study illustrates the power and potential of optimizing computational methods on a large scale of unstructured text data to analyze phenomena in the social field.

Project description:BACKGROUND:Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining. METHOD:Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas. RESULTS:The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions. CONCLUSIONS:Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.

Project description:Introduction:Job coding into a standard occupation or industry classification is commonly performed in occupational epidemiology and occupational health. Sometimes, it is necessary to code jobs into multiple classifications or to convert job codes from one classification to another. We developed a generic tool, called CAPS-Canada (http://www.caps-canada.ca/), that combines a computer-assisted coding tool covering seven International, Canadian and US occupation and industry classifications and an assistant facilitating crosswalks from one classification to another. The objectives of this paper are to present the different functions of the CAPS-Canada tool and to assess their contribution through an inter-rater reliability study. Method:The crosswalk assistant was built based on a database of >30,000 jobs coded during a previous project. We evaluated to what extent it would allow automatic translation between pairs of classifications. The influence of CAPS-Canada on agreement between coders was assessed through an inter-rater reliability study comparing three approaches: manual coding, coding with CAPS-Canada without the crosswalk assistant, and coding with the complete tool. The material for this trial consisted of a random sample of 1000 jobs extracted from a case-control study and divided into three subgroups of equivalent size. Results:Across the classification systems, the crosswalk assistant would provide useful information for 83-99% of jobs (median 95%) in a population similar to ours. Eighteen to eighty-one percent of jobs (median 56%) could be entirely automatically recoded. Based on our sample of 1000 jobs, inter-rater reliability in occupation coding ranged from 35.7 to 66.5% (median 53.7%) depending on the combination of classification/resolution. Compared with manual coding, the use of CAPS-Canada substantially improved inter-rater reliability. Conclusion:CAPS-Canada is an attractive alternative to manual coding and is particularly relevant for coding a job into multiple classifications or for recoding jobs into other classifications.

Project description:Background and objectivePercutaneous coronary intervention (PCI) using drug-eluting stents (DES) is an indispensable treatment for coronary artery disease. However, to evaluate the performance of various types of stents for PCI, numerous resources are required. We extracted clinical information from free-text records and, using practice-based evidence, compared the efficacy of various DES.Materials and methodsWe developed a text mining tool based on regular expression and applied it to PCI reports stored in the electronic health records (EHRs) of Ajou University Hospital from 2010-2014. The PCI data were extracted from EHRs with a sensitivity of 0.996, a specificity of 1.000, and an F-measure of 0.995 when compared with a sample of 200 reports. Using these data, we compared the performance of stents by Kaplan-Meier analysis and the Cox hazard proportional regression.ResultsIn the self-validation analysis comparing the first-generation to the second-generation DES, the second-generation DES was superior to the first-generation DES (hazard ratio [HR]: 0.423, 95% confidence interval [CI]: 0.284-0.630) in terms of target vessel revascularization (TVR), showing similar findings to the established results of previous studies. Among the second-generation DES, the biodegradable-polymer DES tended to be superior, with a risk of TVR (HR: 0.568, 95% CI: 0.281-1.147) falling below than that for the durable-polymer DES approximately 1 year after the index procedure. The Endeavor stent had the highest TVR risk among the newer generation DES (HR: 2.576, 95% CI: 1.273-5.210).ConclusionsIn this study, we demonstrated how to construct a PCI data warehouse of PCI-related parameters obtained from free-text electronic records with high accuracy for use in the post surveillance of coronary stents in a time- and cost effective manner. Post surveillance of the practice based evidence in the PCI data warehouse indicated that the biodegradable-polymer DES might have a lower risk of TVR than the durable-polymer DES.

Dataset Information

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

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

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