Fecha
2022-10-25
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Taylor and Francis, F1000Research
Citas
0 citas en 
0 citas en 
Resumen
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms.
Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake).
Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 26 terms appeared with a frequency of 0.08 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021.
Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.
Descripción
The registered version of this article, first published in Wellcome Open Research, is available online at the publisher's website: Taylor and Francis, F1000Research, https://doi.org/10.12688/wellcomeopenres.16867.5
La versión registrada de este artículo, publicado por primera vez en Wellcome Open Research, está disponible en línea en el sitio web del editor: Taylor and Francis, F1000Research, https://doi.org/10.12688/wellcomeopenres.16867.5
This work was supported by the Wellcome Trust Interna-tional Training Fellowship[214185/Z/18/Z to RMC-L].
La versión registrada de este artículo, publicado por primera vez en Wellcome Open Research, está disponible en línea en el sitio web del editor: Taylor and Francis, F1000Research, https://doi.org/10.12688/wellcomeopenres.16867.5
This work was supported by the Wellcome Trust Interna-tional Training Fellowship[214185/Z/18/Z to RMC-L].
Categorías UNESCO
Palabras clave
public health, health policy, Natural Language Processing, Latin America and the Caribbean, Peru, COVID-19
Citación
Carrillo-Larco RM, Castillo-Cara M and Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters [version 5; peer review: 1 approved, 4 approved with reservations]. Wellcome Open Res 2022, 6:177 (https://doi.org/10.12688/wellcomeopenres.16867.5)
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial