Occupation is considered a Social Determinant of Health (SDOH) and its effects have been studied at multiple levels. Although the inclusion of such data in the Electronic Health Record (EHR) is vital for the provision of clinical care, specially in rheumatology where work disability prevention is essential, occupation information is often either not routinely documented or captured in an unstructured manner within conventional EHR systems. Encouraged by recent advances in natural language processing and deep learning models, we propose the use of novel architectures (i.e., transformers) to detect occupation mentions in rheumatology clinical notes of a tertiary hospital, and to whom those occupations belongs. We also aimed to evaluate the clinical and demographic characteristics that influence the collection of this SDOH; and the association between occupation and patients’ diagnosis. Bivariate and multivariate logistic regression analysis were conducted for this purpose.

A Spanish pre-trained language model, RoBERTa, fine-tuned with biomedical texts was used to detect occupations. The best model achieved a F1-score of 0.725 identifying occupation mentions. Moreover, highly disabling mechanical pathology diagnoses (i.e., back pain, muscle disorders) were associated with a higher probability of occupation collection. Ultimately, we determined the professions most closely associated with more than ten categories of muscu-loskeletal disorders.

Highlights

Deep learning models hold significant potential for structuring and leveraging information in rheumatology

Diagnoses related to highly disabling mechanical pathology were associated with a higher probability of occupation collection

Cleaners, helpers, and social workers occupations are linked to mechanical pathologies such as back pain

The authors have declared no competing interest.

This work was supported by the Instituto de Salud Carlos III, Ministry of Health, Madrid, Spain [RD21/002/0001].

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

HCSC Ethics Review Board approval (23/340-E) was obtained as a retrospective study and waiver of informed consent was obtained for the use of unidentified clinical records. Furthermore, the study was conducted in accordance with the Declaration of Helsinki.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

↵1 First author

This work was supported by the Instituto de Salud Carlos III, Ministry of Health, Madrid, Spain [RD21/002/0001]. The sponsor or funding organization had no role in the design or conduct of this research. The journal’s Fee was funded by the institution employing the senior author of the manuscript (Fundación Biomédica del Hospital Clínico San Carlos)

The authors declare there are no competing interests