Application of Artificial Intelligence with Natural Language Processing for qualitative research texts in the medical-patient relationship with mental illness through the use of mobile technologies
DOI:
https://doi.org/10.35669/rcys.2020.10(1).19-41Keywords:
artificial intelligence, natural language processing, machine learning, communication, social science, mHealth, mental healthAbstract
Artificial Intelligence (AI) continues to position itself in society as a benchmark for technological progress. Within this field, Natural Language Processing (NLP) reaches great acceptance in disciplines that work with high volumes of data (Big Data). In this framework we want to see what do these algorithms contribute with, but applied to communication in the field of mental health. We establish this methodology with NLP based on previous qualitative observations in transcribed texts of focus groups. These texts were obtained from focus groups carried out on patients with mental illnesses in order to understand whether the application of this methodology contributes to any improvement on the analysis of data, which has been shown in previous researches. However, this research has been applied in a novel way in the field of mental health. To do this, scripts based on Python code have been executed and the texts have been purified, classifying the word strings into entities called tokens and eliminating stopwords. Subsequently, the frequency of words and the connection of sentences have been analyzed, obtaining a set of structures in which to apply Machine Learning techniques using word2vec and generating vectors on the data, which are represented with n-dimensional graphics where a new vocabulary based on proximity words is created. We are applying a method that without algorithmic learning we would be unable to obtain this type of information in the previous analysis of qualitative research.The main themes found with traditional qualitative analysis are identified in the analysis, mechanizing the process and facilitating it. It is also shown that this methodology is applicable in mental health as in other population groups.
Downloads
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467. Disponible en: https://bit.ly/3bQSEZm
Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth (No. w23928). National Bureau of Economic Research. Disponible en: https://bit.ly/2VNLUWL
Bertoldi, S., Fiorito, M. E., & Álvarez, M. (2006). Grupo Focal y Desarrollo local: aportes para una articulación teórico-metodológica. Ciencia, docencia y tecnología, 17(33), 111-131. Disponible en: https://bit.ly/2yRjRwr
Brunn, M., Diefenbacher, A., Courtet, P., & Genieys, W. (2020). The Future is Knocking: How Artificial Intelligence Will Fundamentally Change Psychiatry. Academic Psychiatry, Online. Disponible en: https://bit.ly/3dUvEcQ
Bustos, A., Pertusa, A., Salinas, J. M., & de la Iglesia-Vayá, M. (2019). Padchest: A large chest x-ray image dataset with multi-label annotated reports. arXiv preprint arXiv:1901.07441. Disponible en: https://bit.ly/2KLyjbX
Calero, J. L. (2000). Investigación cualitativa y cuantitativa. Problemas no resueltos en los debates actuales. Rev. Cubana Endocrinol, 11(3), 192-8. Disponible en: https://go.aws/2yUCgbA
Gibbs, A. (1997). Focus groups. Social research update, 19(8), 1-8. Disponible en:
Guetterman, T. C., Chang, T., DeJonckheere, M., Basu, T., Scruggs, E., & Vydiswaran, V. (2018). Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study. Journal of medical Internet research, 20(6), e231. Disponible en: https://bit.ly/3aP3ded
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), 30–36. Disponible en: https://bit.ly/3h6L4go
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266. Disponible en: https://stanford.io/3cGRU8J
Liddy, E. D. (2001). Natural language processing. Disponible en: https://bit.ly/2zHhpJp
Loper, E., & Bird, S. (2002). NLTK: the natural language toolkit. arXiv preprint cs/0205028. Disponible en: https://bit.ly/2VJV1Yi
Lupiáñez-Villanueva, F. (2011). Salud e internet: más allá de la calidad de la información. Revista española de cardiología, 64(10), 849-850. Disponible en: https://bit.ly/3bS3SwM
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119). Disponible en: https://bit.ly/2KI7CVF
Morgan, D. L., Krueger, R. A., & Scannell, A. U. (1998). Planning focus groups. Sage. Disponible en: https://bit.ly/2Soo69A
Perrault, R., Shoham Y., Brynjolfsson E., Clark J., Etchemendy J., Grosz, B., Lyons, T., Manyika T., Mishra, S., & Niebles J.C. (2019). “The AI Index 2019 Annual Report”, AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, December 2019. Disponible en: https://stanford.io/2KFNLGN
Powell, R. A., & Single, H. M. (1996). Focus groups. International journal for quality in health care, 8(5), 499-504. Disponible en: https://bit.ly/3aKAWFt
Rodriguez, M., Sivic, J., Laptev, I., & Audibert, J. Y. (2011, November). Data-driven crowd analysis in videos. In 2011 International Conference on Computer Vision (pp. 1235-1242). IEEE. Disponible en: https://bit.ly/2SfWiV1
Rong, G., Mendez, A., Assi, E. B., Zhao, B., & Sawan, M. (2020). Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering. Disponible en: https://bit.ly/30mAgEO
Steckler, A., McLeroy, K. R., Goodman, R. M., Bird, S. T., & McCormick, L. (1992). Toward Integrating Qualitative and Quantitative Methods: An Introduction. Health Education Quarterly, 19(1), 1–8. Disponible en: https://bit.ly/2YfU6k7
Taylor, S. J., & Bogdan, R. (1987). Introducción a los métodos cualitativos de investigación (Vol. 1). Barcelona: Paidós. Disponible en: https://go.aws/2SfsA2b
Turney, L., & Pocknee, C. (2005). Virtual focus groups: New frontiers in research. International Journal of Qualitative Methods, 4(2), 32-43. Disponible en: https://bit.ly/3bPzByB
Webster, J. J., & Kit, C. (1992). Tokenization as the initial phase in NLP. In COLING 1992 Volume 4: The 15th International Conference on Computational Linguistics. Disponible en: https://bit.ly/2SktsCP
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Revista de Comunicación y Salud
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Disclaimer: As of 2023, RCyS is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Articles from 2011 to 2022 are under a Creative Commons CC BY 4.0 License that allows copying and redistributing the material in any medium or format, remixing, transforming and building upon the material for any purpose, including commercial.
We also inform that RCyS is adhered to the Declaration of Berlin.
