A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome

Abstract

The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a more holistic approach on the exploration of the relationship between the environment and disease. At the same time, further and more dramatic changes have also occurred on the working environment, adding to the already existing dynamic nature of it. Natural Language Processing (NLP) refers to a collection of methods for identifying, reading, extracting and untimely transforming large collections of language. In this work, we aim to give an overview of how NLP has successfully been applied thus far in Exposome research. Methods: We conduct a literature search on PubMed, Scopus and Web of Science for scientific articles published between 2011 and 2021. We use both quantitative and qualitative methods to screen papers and provide insights into the inclusion and exclusion criteria. We outline our approach for article selection and provide an overview of our findings. This is followed by a more detailed insight into selected articles. Results: Overall, 6420 articles were screened for the suitability of this review, where we review 37 articles in depth. Finally, we discuss future avenues of research and outline challenges in existing work. Conclusions: Our results show that (i) there has been an increase in articles published that focus on applying NLP to exposure and epidemiology research, (ii) most work uses existing NLP tools and (iii) traditional machine learning is the most popular approach.

Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6dof Inertial Measurement Units}

“…A solution for time series estimation is using a deep learning model that can learn the relationship between the input and output data, called sequential modeling. Sensor data, text, sound, and specific data with an underlying sequential structure can be handled with sequence models for several applications, including time series data prediction [74], speech recognition [75], natural language processing [76], music generation [77], and DNA sequence analysis [78]. The traditional neural network models cannot handle time-series data as they do not loop and handle time dependencies between them.…”

Bibliometric analysis of the top 100 most-cited articles on video laryngoscope from 2011 to 2022

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“…This extracted information is then used to generate a summary paragraph for a quick literature review highlighting key findings and trends. 6…”

End-to-End Deep Learning Framework for Real-Time Inertial Attitude Estimation using 6DoF IMU

“…(2020), speech recognitionGraves et al (2013), natural language processingSchoene et al (2022), music generationMarinescu (2019), and DNA sequence analysisShen et al (2018). The traditional neural network models cannotF I G U R E 1 Loss functions for attitude error (a) Compare Loss functions for attitude error (b) Compare Loss functions derivative for attitude error handle time-series data as they do not loop and handle time dependencies between them.…”