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An Artificial Intelligence Approach to Modeling in Social Science

Received: 4 January 2021     Accepted: 20 February 2021     Published: 17 March 2021
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Abstract

Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.

Published in Journal of Health and Environmental Research (Volume 7, Issue 1)

This article belongs to the Special Issue Health and the Environment as a Resource for the Reduction of Social Inequalities in Argentina

DOI 10.11648/j.jher.20210701.20
Page(s) 58-68
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Artificial Neural Network, Family Clinic History, Risk, Health, Housing

References
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[2] PAHO, Community Health in the XXI Century, Consulted on date 26/10/2020, https://www.paho.org/hq/index.php?option=com_content&view=article&id=13491:community-health-in-the-21st-century&Itemid=40283&lang=es.
[3] Argentina.gob.ar, “Salud Ambiental”, Consulted on date 26/10/2020, https://www.argentina.gob.ar/salud/ambiental.
[4] Argentina.gob.ar, “Salud Ambiental”, Consulted on date 26/10/2020, https://www.argentina.gob.ar/salud/comunitaria.
[5] Rojas, M. C. “La vivienda precaria urbana marginal y su relación con la salud de la población en el proceso de sustentabilidad. Un enfoque teórico para la estimación del riesgo y la vulnerabilidad”. First Congress of the Latin American Population Association -ALAP-, CD-ROM. Brazilian Association of Population Studies -ABEP- [Presentation] Caxambu –MG- Brazil; 2004.
[6] Rojas, M. C. “Población, vivienda salud y vulnerabilidad global. Propuesta teórico-metodológica para la estimación del riesgo de la vivienda urbana para la salud basada en el análisis de la vulnerabilidad sociodemográfica.” [doctoral thesis] Faculty of Economic Sciences of the National University of Cordoba; Cordoba, Argentina; 2006.
[7] Cardona Arboleda, O. D. “Estimación holística del riesgo sísmico utilizando sistemas dinámicos complejos”, Barcelona, España, 2001. Consulted on date: 01/10/2020, https://repositorio.gestiondelriesgo.gov.co/bitstream/handle/20.500.11762/19751/HolisticaRiesgoSismicoBogota(Cardona_2001).pdf?sequence=1.
[8] IIGHI-CONICET, NRVAS Line of Research, Consulted on date: 01/10/2020, https://iighi.conicet.gov.ar/nucleos/nrvas/#:~:text=La%20Red%20Interamericana%20de%20Vivienda%20Saludable%20(Red%20VIVSALUD)%20es%20una,la%20Regi%C3%B3n%20de%20las%20Am%C3%A9ricas.
[9] Chen, T., & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794); San Francisco, California, USA. August 2016. https://doi.org/10.1145/2939672.2939785.
[10] Harris, C. R., Millman, K. J., van der Walt, S. J. et al., Array programming with NumPy; Nature 585, 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2.
[11] Lundberg, S. M., Erion, G., Chen, H. et al., From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2, 56–67 (2020). https://doi.org/10.1038/s42256-019-0138-9.
[12] McWinney, I. “Medicina de Familia”, Ed. Doyma. Barcelona, Spain; 2000.
[13] Rojas, M., Vazquez J., Castillo J., Cardenas M. “Modelado del Riesgo de la Vivienda Urbana para la Salud y el empleo de Redes Neuronales Artificiales para su Estimación”, JAIIO 2010, Workshop CASI, Cordoba, Argentina, 2010.
[14] Frittelli, V., Steffolani, F. A., Teicher, R. G., Rojas, M., Picco, J. E., Vazquez, J. C. “Búsqueda por Similaridad aplicada en la Recuperación de Factores que inciden en el Cálculo del Índice de Riesgo para la Salud de la Vivienda Urbana”, 40 JAIIO – CASI, Cordoba, Argentina, 2011. Query at 01/10/2020: http://40jaiio.sadio.org.ar/sites/default/files/T2011/CAIS/CAIS2011-28.pdf.
[15] Goodfellow I., Bengio Y., Courville A., “Deep Learning”, MIT Press, NY, USA, 2016. (www.deeplearningbook.org).
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Cite This Article
  • APA Style

    Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez. (2021). An Artificial Intelligence Approach to Modeling in Social Science. Journal of Health and Environmental Research, 7(1), 58-68. https://doi.org/10.11648/j.jher.20210701.20

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    ACS Style

    Juan Carlos Jesus Vazquez; Julio Javier Castillo; Leticia Edith Constable; Marina Elizabeth Cardenas; Juan Carlos Guillermo Vazquez. An Artificial Intelligence Approach to Modeling in Social Science. J. Health Environ. Res. 2021, 7(1), 58-68. doi: 10.11648/j.jher.20210701.20

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    AMA Style

    Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez. An Artificial Intelligence Approach to Modeling in Social Science. J Health Environ Res. 2021;7(1):58-68. doi: 10.11648/j.jher.20210701.20

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  • @article{10.11648/j.jher.20210701.20,
      author = {Juan Carlos Jesus Vazquez and Julio Javier Castillo and Leticia Edith Constable and Marina Elizabeth Cardenas and Juan Carlos Guillermo Vazquez},
      title = {An Artificial Intelligence Approach to Modeling in Social Science},
      journal = {Journal of Health and Environmental Research},
      volume = {7},
      number = {1},
      pages = {58-68},
      doi = {10.11648/j.jher.20210701.20},
      url = {https://doi.org/10.11648/j.jher.20210701.20},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jher.20210701.20},
      abstract = {Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.},
     year = {2021}
    }
    

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    T1  - An Artificial Intelligence Approach to Modeling in Social Science
    AU  - Juan Carlos Jesus Vazquez
    AU  - Julio Javier Castillo
    AU  - Leticia Edith Constable
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    T2  - Journal of Health and Environmental Research
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    JO  - Journal of Health and Environmental Research
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    AB  - Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
    VL  - 7
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Author Information
  • Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina

  • Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina

  • Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina

  • Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina

  • Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina

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