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Occupational Health Informatics is the field concerned with the optimal use of information to maximize occupational health and well-being, achieved by multidisciplinary professionals using appropriate technologies. The occupational health community should advocate for the recording of occupation in electronic health record systems. Data linkages can be created between occupation, disease, function and exposures to inform occupational functionality, recognition and recording of occupational diseases, disability accommodation and work as a health outcome.

Objectives Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. Methods We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. Results The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8–58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69–0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). Conclusions In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.

The DNA fingerprinting techniques, 16S-restriction fragment length polymorphism (16S-RFLP), ribosomal intergenic spacer analysis (RISA) and repetitive extragenic palindrome PCR (Rep-PCR), were used for analyzing the bacterial communities of seven pulp and paper wastewater treatment systems. All three methods generate DNA fingerprints that can be compared using the computer-assisted program, Gelcompar©. Community similarity coefficients were based on quantitative determinations of both the positions of the DNA bands and the band intensities in order to compare the relative differences in the populations. Unique 16S-RFLP DNA fingerprints were observed for each mill suggesting that individual mills contained phylogenetically different communities. However this method was not sensitive enough to detect differences within a mill treatment system from different locations or from different sampling times. The RISA method, which generated more complex fingerprints than 16S-RFLP, could, for some mills, discern differences between samples. The Rep-PCR technique, however, showed the highest degree of resolution and produced not only distinct patterns for each mill but also distinct fingerprints for the temporal and spatial samples from some of the treatment systems. The sensitivity of this method might potentially be used to monitor the stability of the bacterial community within a secondary treatment system.

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Introduction Information about occupations and job attributes is available in siloed databases. The lack of integrated data precludes ad-hoc querying and research investigating occupational determinants of health e.g. COVID-19 or stress. Core to integration of occupation data is taxonomic representation of job categories. In North America the official occupational taxonomies are the Canadian National Occupational Classification (NOC) and the United States Standard Occupational Classification (SOC). Objectives This study aimed to integrate job attribute data from the Canadian Career Handbook (CH) and O*NET database to facilitate cross-classification query capabilities and to prototype the creation of metrics for comparing occupations based on job attributes. Methods The integrated database was completed hierarchical structures of both occupational taxonomies were represented; job attributes were selected from the CH and O*NET-SOC; the database was populated with occupational descriptions; occupational codes from the CH and O*NET-SOC were linked using the Brookfield Institute NOC to O*NET-SOC crosswalk. Results The database consists of 1679 rows with unique occupations and 181 columns with occupational attributes. Rows contain a unique combination of hierarchical structures from the CH and O*NET-SOC. Rows also contain detailed occupational descriptions from CH and O*NET-SOC. We queried the integrated data checking O*NET-SOC to CH equivalence and cross-taxonomy selection of job attributes, e.g. Retrieve all or selected attributes for an occupation by CH code or equivalent code in O*NET-SOC. We ran queries for targeted scenarios to retrieve occupations: i) where work is done in physical proximity to others, ii) where incumbents are exposed to disease or infections, iii) at risk of back pain due to physical work factors, iv) where incumbents experience high work-related stressors. Conclusion We report a database combining selected information from the CH and O*NET-SOC that facilitates complex occupational health queries. Further we investigated work-related stressors on low back pain risk by occupation.

Introduction Occupational data in prospective cohort studies is often underutilized due to the human and financial resources required to code open-ended text, such as job titles. Recognizing the value of occupational data in health research, as well as potential errors associated with manual coding, an Automated Coding Algorithm (ACA)-NOC algorithm was developed utilizing a Natural Language Processing approach. Objectives We tested the ACA-NOC algorithm on two regional cohorts of a pan-Canadian cohort study, which represents the largest dataset an algorithm of this kind has been applied to. This process will harmonize and greatly expand the utility of the occupational data, enrich the research platforms, and further refine the efficiency of the algorithm. Methods The ACA-NOC algorithm was tested on data from the Canadian Partnership for Tomorrow’s Health (CanPath), a longitudinal cohort examining the role of genetic, environmental, lifestyle, and behavioural factors in the development of cancer and chronic disease. Using an iterative and interactive approach, the algorithm was applied to job title data from 111,000 questionnaires from two regional cohorts, coding the data to the Canadian National Occupation Classification (NOC) system. The algorithm was further refined based on each round of analysis, increasing the quantity of accurately coded data. Results Results from this research demonstrate the ability to refine the ACA-NOC algorithm with a 10% overall improvement in exact matching from the baseline algorithm. There were also instances where the algorithm performance was superior to the manual coding. The utilization of the algorithm offers significant savings in time, human resources and cost compared to a singular manual coding approach. Conclusions The coding and harmonization of this multi-cohort data demonstrates the value of the ACA-NOC algorithm, while increasing the utility of the CanPath data and research related to occupational health. Future research may involve comparisons between CanPath and international cohorts.

Introduction Occupational encoding is a technique that allows job titles provided by study participants to be categorized according to their role in the labor force. Encoding has primarily been a slow error-prone manual process which is ripe for automation. Objectives Our goals was to design and test an automated coding prototype using machine learning techniques. Methods The prototype classification system ENENOC (the ENsemble Encoder for the National Occupational Classification) is comprised of series of steps involving data cleaning, exact match search, multi classifier ensembling, hierarchical classification, and multiple output selection. In the absence of exact matching between job title input and NOC category descriptions, the input data is embedded using the TF-IDF algorithm and Doc2Vec. The embeddings are fed into a hierarchical, ensemble classifier that uses classical machine learning techniques: Random Forests, Support Vector Machine and K-Nearest Neighbour. Ensemble encoding is achieved using a majority-voting system. The hierarchical two tier classification methodology first predicts the first digit of the NOC code followed while the second tier predicts the second third and fourth digit of the NOC code for the input data. The combined approach produces a single, 4-digit code as a top choice, as well as four alternate NOC codes, that serve as additional ranked choice based on the Doc2Vec model. Results The prototype was benchmarked on a manually annotated data set comprising of 64,000 records. It produced a top-1 Per-Digit Macro F1-Score of 0.65 and a top-5 Per-Digit Macro F1-Score of 0.76, both of which are highly within published accuracy ranges for manual coding (44% to 89% inter-annotator agreement). ENENOC coded 30,000 job titles in 3 hours. Conclusion The ENENOC prototype is a sophisticated ENsemble Encoder for the National Occupational Classification which has state of the art performance accuracy with significant speed improvements over manual coding.

Global health surveillance and pandemic intelligence rely on the systematic collection and integration of data from diverse distributed and heterogeneous sources at various levels of granularity. These sources include data from multiple disciplines represented in different formats, languages, and structures posing significant integration challenges This article provides an overview of challenges in data driven surveillance. Using Malaria surveillance as a use case we highlight the contribution made by emerging semantic data federation technologies that offer enhanced interoperability, interpretability and explainability through the adoption of ontologies. The paper concludes with a focus on the relevance of these technologies for ongoing pandemic preparedness initiatives.

This paper reports on the early-stage development of an analytics framework to support the semantic integration of dynamic surveillance data across multiple scales to inform decision making for malaria eradication. We propose using the Semantic Web of Things (SWoT), a combination of Internet of Things (IoT) and semantic web technologies, to support the evolution and integration of dynamic malaria data sources and improve interoperability between different datasets generated through relevant IoT assets (e.g. computers, sensors, persons, and other smart objects and devices).

Informational needs of agricultural consultants are increasingly complex. Advising farmers on the appropriate measures for optimizing cropping yields demands access to custom data archives and analytics tools. In line with the increasing number of archives, the expertise required of consultants goes beyond the capabilities of these non-technical agri-specialists. These end users have diverse ad-hoc query needs and require tools that provide simple access to distributed data silos and easy ways to integrate relevant information. In this article, the authors report on a pilot deployment of Semantic Automated Discovery and Integration (SADI) Web services for the federation and computation of agricultural data. A registry of 9 SADI Web services was deployed to expose data from a variety of different data resources in support of a defined set of query needs. The authors demonstrate that the deployment of these services facilitates the ad-hoc creation and execution of mission critical workflows targeting use cases in agricultural operations management. Using HYDRA, a semantic query engine for SADI Web services with a custom built graphical user interface, agricultural consultants can identify optimal crop varieties, and compute profit margins of each variety using a complex cost model.

BACKGROUND In many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. Such information is usually solicited during interviews with open-ended questions such as “What is your job?” and “What industry sector do you work in?” Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation. OBJECTIVE This study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm. METHODS We developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes. RESULTS The ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7% (332/566) and improved when broader job categories were considered (65.0% at the three-digit NOC code level, 72.3% at the two-digit NOC code level, and 81.6% at the one-digit NOC code level). CONCLUSIONS The ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets.

Background In many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. Such information is usually solicited during interviews with open-ended questions such as “What is your job?” and “What industry sector do you work in?” Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation. Objective This study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm. Methods We developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes. Results The ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7% (332/566) and improved when broader job categories were considered (65.0% at the three-digit NOC code level, 72.3% at the two-digit NOC code level, and 81.6% at the one-digit NOC code level). Conclusions The ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets.