Topic Modeling through unsupervised Machine Learning of scientific articles on Occupational Health and Home Care Services

Authors

  • Ruben Palomo Llinares Universidad Miguel Hernandez https://orcid.org/0000-0002-1890-4337
  • Julia Sánchez-Tormo Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante

DOI:

https://doi.org/10.22585/hospdomic.v7i4.200

Keywords:

Occupational Health, Home Care Services, topic modeling, sentiment analysis

Abstract

Objective: To identify in an unsupervised manner through topic modeling the topics of greatest interest in the field of Occupational Health and Home Care Services from the scientific articles published on the subject.

Method: The study used the unsupervised Machine Learning algorithm Dirichlet Latent Assignment for topic modeling and the NRC lexicon to carry out the sentiment analysis of the corpus of document files obtained from MEDLINE (via PubMed) using the descriptors “Occupational Health” and “Home Care Services”.

Results: Of the total of 70 documentary files analyzed, it was obtained that the intensity of the emotions in the texts was low (ranging in values from 5 to 10), with positive feelings having a greater representation compared to negative ones in a ratio of 60/ 40. There was no variation in the proportions of emotions with respect to the study period. The four topics of greatest interest were identified in the articles analyzed: home care and caregiver satisfaction, breastfeeding period, rehabilitation programs, and physical activity to mitigate pain.

Conclusions: It has been confirmed that natural language processing methodologies can be a great support tool for the analysis of scientific articles. Specifically, it has been possible to determine in a clear and unsupervised manner the topics of greatest interest in the field of Occupational Health and Home Care Services.

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References

Zippel C, Bohnet-Joschko S. Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov. Int J Environ Res Public Health. 2021;18(10):5072. DOI: 10.3390/ijerph18105072

Ngai EWT, Wu Y. Machine learning in marketing: A literature review, conceptual framework, and research agenda. J Bus Res. 2022;145:35-48. DOI: 10.1016/j.jbusres.2022.02.049

Buczynski W, Cuzzolin F, Sahakian B. A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life. Int J Data Sci Anal. 2021;11(3):221-42. DOI: 10.1007/s41060-021-00245-5

Buchanan BG, Wright D. The impact of machine learning on UK financial services. Oxf Rev Econ Policy. 2021;37(3):537-63. DOI: 10.1093/oxrep/grab016

Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health Care Inform. 2021;28(1):e100262. DOI: 10.1136/bmjhci-2020-100262

Análisis de sentimiento. En: Wikipedia, la enciclopedia libre [Internet]. 24 de mayo de 2023 [citado 1 de octubre de 2023]. Recuperado: http://bit.ly/46B9uro

Topic model. En: Wikipedia [Internet]. 17 de septiembre de 2023 [citado 1 de octubre de 2023]. Recuperado: https://bit.ly/48CXwiA

Lutz AM, Lutz R. Topic Modeling as an evaluation basis in literature research - A proposal for a new literature review method for machine-assisted source evaluation using the example of anthropology. Anthropol Anz Ber Uber Biol-Anthropol Lit. 2023;80(2):119-34. DOI: 10.1127/anthranz/2023/1660

Alexander G, Bahja M, Butt GF. Automating Large-scale Health Care Service Feedback Analysis: Sentiment Analysis and Topic Modeling Study. JMIR Med Inform. 2022;10(4):e29385. DOI: 10.2196/29385

Báez P, Arancibia AP, Chaparro MI, Bucarey T, Núñez F, Dunstan J. Procesamiento de lenguaje natural para texto clínico en español: el caso de las listas de espera en Chile. Rev Médica Clínica Las Condes. 2022;33(6):576-82. DOI: 10.1016/j.rmclc.2022.10.002

Scarpino I, Zucco C, Vallelunga R, Luzza F, Cannataro M. Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration. Biotech Basel Switz. 2022;11(3):41. DOI: 10.3390/biotech11030041

Muchene L, Safari W. Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. PloS One. 2021;16(1):e0243208. DOI: 10.1371/journal.pone.0243208

Kavvadias S, Drosatos G, Kaldoudi E. Supporting topic modeling and trends analysis in biomedical literature. J Biomed Inform. 2020;110:103574. DOI: 10.1016/j.jbi.2020.103574

Liu L, Tang L, Dong W, Yao S, Zhou W. An overview of topic modeling and its current applications in bioinformatics. SpringerPlus. 2016;5(1):1608. DOI: 10.1186/s40064-016-3252-8

Xue J, Zhang B, Zhang Q, Hu R, Jiang J, Liu N, et al. Using Twitter-Based Data for Sexual Violence Research: Scoping Review. J Med Internet Res. 2023;25:e46084. DOI: 10.2196/46084

Ong S-Q, Pauzi MBM, Gan KH. Text mining in mosquito-borne disease: A systematic review. Acta Trop. 2022;231:106447. DOI: 10.1016/j.actatropica.2022.106447

Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. J Med Syst. 2020;44(12):205. DOI: 10.1007/s10916-020-01669-5

Tao D, Yang P, Feng H. Utilization of text mining as a big data analysis tool for food science and nutrition. Compr Rev Food Sci Food Saf. 2020;19(2):875-94. DOI: 10.1111/1541-4337.12540

Stefanis C, Giorgi E, Kalentzis K, Tselemponis A, Nena E, Tsigalou C, et al. Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models. Front Public Health. 2023;11:1191730. DOI: 10.3389/fpubh.2023.1191730

O’Sullivan JN. Trends in population health and demography. The Lancet. Elsevier; 2021;398(10300):580. DOI: 10.1016/S0140-6736(21)01050-3

Hanley E. The role of home care in palliative care services. Care Manag J J Case Manag J Long Term Home Health Care. 2004;5(3):151-7. DOI: 10.1891/cmaj.2004.5.3.151

Seow H, Barbera L, Howell D, Dy SM. Using more end-of-life homecare services is associated with using fewer acute care services: a population-based cohort study. Med Care. 2010;48(2):118-24. DOI: 10.1097/MLR.0b013e3181c162ef

Elliott J, Gordon A, Tong CE, Stolee P. «We’ve got the home care data, what do we do with it?»: understanding data use in decision making and quality improvement. BMC Health Serv Res. 2020;20(1):251. DOI: 10.1186/s12913-020-5018-9

Kim E-Y, Cho E, June KJ. Factors influencing use of home care and nursing homes. J Adv Nurs. 2006;54(4):511-7. DOI: 10.1111/j.1365-2648.2006.03839.x

Chang M, Michelet M, Skirbekk V, Langballe EM, Hopstock LA, Sund ER, et al. Trends in the use of home care services among Norwegians 70+ and projections towards 2050: The HUNT study 1995-2017. Scand J Caring Sci. 2023;37(3):752-65. DOI: 10.1111/scs.13158

Canada G of CNRC. [Internet]. 3 de octubre de 2023. NRC emotion lexicon - NRC Publications Archive [citado 3 de octubre de 2023]. Disponible en: https://bit.ly/46eRlQe

Mohammad SM, Turney P. [Internet]. NRC Emotion Lexicon [consultado 29 de septiembre de 2023]. Disponible en: https://bit.ly/3F1RAlH

Silvestre Gómez M. Implementación de asignación jerárquica latente de Dirichlet para modelado de temas. 2018 [citado 29 de septiembre de 2023]; Recuperado: https://bit.ly/3PZndCM

Ley 14/2007, de 3 de julio, de Investigación biomédica, BOE núm. 159 [Internet]. [citado 10 de septiembre de 2023]. Recuperado: https://www.boe.es/eli/es/l/2007/07/03/14

Meza P. El posicionamiento estratégico del autor en artículos de investigación: un modelo empíricamente fundado. Logos Rev Lingüíst Filos Lit. 2017;27(1):152-64. DOI: 10.15443/RL2711

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Published

2023-11-27

How to Cite

Palomo Llinares, R., & Sánchez-Tormo, J. (2023). Topic Modeling through unsupervised Machine Learning of scientific articles on Occupational Health and Home Care Services. Hospital a Domicilio, 7(4), 167–178. https://doi.org/10.22585/hospdomic.v7i4.200

Issue

Vol. 7 No. 4 (2023)

Section

Artículos originales

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Copyright (c) 2023 Ruben Palomo Llinares, Julia Sánchez-Tormo

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