USO Y APLICACIÓN DEL APRENDIZAJE AUTOMÁTICO PARA CREAR MODELOS EN LA TOMA DE DECISIONES EN EL ÁREA DE LA SALUD

Denis Cedeño

doi:10.48204/3072-9696.6355

Submitted November 18, 2024

Published 2024-11-26

Artículos

Vol. 1 No. 1 (2024): + TIC Journal

USE AND APPLICATION OF MACHINE LEARNING TO CREATE MODELS IN DECISION MAKING IN THE HEALTH AREA

Denis Cedeño⁺⁻

DOI https://doi.org/10.48204/3072-9696.6355

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DOI: 10.48204/3072-9696.6355

Published: 2024-11-26

Abstract

Artificial intelligence (AI) is the discipline that tries to replicate and develop intelligence and its implicit processes through computers. AI synthesizes and automates tasks that are intellectual in principle and is therefore potentially relevant to any field of human intellectual activity, currently encompassing a wide variety of subfields, such as natural language processing (NLP), machine learning (ML), robotics. With AI, data analysis projects are being carried out working with ML, however, these types of solutions are very scarce, especially in the medical area of ??our country. Using innovative technologies such as AI applied to areas as sensitive as health is increasing every day. The new models based on ML are currently being used more, however, in our countries there are few studies related to the subject. Therefore, this research aims to use various ML techniques and determine how these models can help us solve health problems.

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References

Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-Based Learning Algorithms. Machine Learning, 6(1), 37–66. https://doi.org/10.1023/A:1022689900470
Akusok, A., Bjork, K.-M., Miche, Y., & Lendasse, A. (2015). High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access, 3, 1011–1025. https://doi.org/10.1109/ACCESS.2015.2450498
Cedeno-Moreno, D., & Vargas-lombardo, M. (2016). Towards A Model Of Knowledge Extraction Of Text Mining For Palliative Care Patients In. International Journal of Scientific & Technology Research, 5(07).
Fadly, A. El, Daniel, C., Bousquet, C., Dart, T., Lastic, P.-Y., & Degoulet, P. (2007). Electronic Healthcare Record and Clinical Research in Cardiovascular Radiology. HL7 CDA and CDISC ODM Interoperability. AMIA 2007 Symposium Proceedings, 216–220. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655824/pdf/amia-0216-s2007.pdf
Khairnar, J., & Kinikar, M. (2013). Machine Learning Algorithms for Opinion Mining and Sentiment Classification. International Journal of Scientific and Research Publications, 3(6), 1–6. www.ijsrp.org
Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(September), 354–368. https://doi.org/10.1016/j.dss.2009.09.003
Maier, R. (2007). Knowledge Management Systems: Information and Communication Technologies for Knowledge Management. Knowledge Management, 2, 720. https://doi.org/10.1007/978-3-540-71408-8_10
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McCallum, a., Nigam, K., Rennie, J., & Seymore, K. (1999). Building domain-specific search engines with machine learning techniques. Proc. AAAI-99 Spring Symposium on Intelligent Agents in Cyberspace, 1999. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.4708&rep=rep1&type=pdf
Mcgregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004). Flow Clustering Using Machine Learning Techniques. 205–214.
Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering, 06(05), 551–560. https://doi.org/10.4236/jbise.2013.65070
Ordoñez-Salinas, S., & Gelbukh, A. (2010). Information retrieval with a simplified conceptual graph-like representation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6437 LNAI(PART 1), 92–104. https://doi.org/10.1007/978-3-642-16761-4_9
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2012). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://doi.org/10.1007/s13398-014-0173-7.2
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Trstenjak, B., Mikac, S., & Donko, D. (2014). KNN with TF-IDF based framework for text categorization. Procedia Engineering, 69, 1356–1364. https://doi.org/10.1016/j.proeng.2014.03.129
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Zainuddin, N., & Selamat, A. (2014). Sentiment analysis using Support Vector Machine. I4CT 2014 - 1st International Conference on Computer, Communications, and Control Technology, Proceedings, May 2016, 333–337. https://doi.org/10.1109/I4CT.2014.6914200

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[1] Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-Based Learning Algorithms. Machine Learning, 6(1), 37–66. https://doi.org/10.1023/A:1022689900470

[2] Akusok, A., Bjork, K.-M., Miche, Y., & Lendasse, A. (2015). High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access, 3, 1011–1025. https://doi.org/10.1109/ACCESS.2015.2450498

[3] Cedeno-Moreno, D., & Vargas-lombardo, M. (2016). Towards A Model Of Knowledge Extraction Of Text Mining For Palliative Care Patients In. International Journal of Scientific & Technology Research, 5(07).

[4] Fadly, A. El, Daniel, C., Bousquet, C., Dart, T., Lastic, P.-Y., & Degoulet, P. (2007). Electronic Healthcare Record and Clinical Research in Cardiovascular Radiology. HL7 CDA and CDISC ODM Interoperability. AMIA 2007 Symposium Proceedings, 216–220. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655824/pdf/amia-0216-s2007.pdf

[5] Khairnar, J., & Kinikar, M. (2013). Machine Learning Algorithms for Opinion Mining and Sentiment Classification. International Journal of Scientific and Research Publications, 3(6), 1–6. www.ijsrp.org

[6] Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(September), 354–368. https://doi.org/10.1016/j.dss.2009.09.003

[7] Maier, R. (2007). Knowledge Management Systems: Information and Communication Technologies for Knowledge Management. Knowledge Management, 2, 720. https://doi.org/10.1007/978-3-540-71408-8_10

[8] Maldonado, J. A., Costa, C. M., Moner, D., Menárguez-Tortosa, M., Boscá, D., Miñarro Giménez, J. A., Fernández-Breis, J. T., & Robles, M. (2012). Using the ResearchEHR platform to facilitate the practical application of the EHR standards. Journal of Biomedical Informatics, 45(4), 746–762. https://doi.org/10.1016/j.jbi.2011.11.004

[9] McCallum, a., Nigam, K., Rennie, J., & Seymore, K. (1999). Building domain-specific search engines with machine learning techniques. Proc. AAAI-99 Spring Symposium on Intelligent Agents in Cyberspace, 1999. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.4708&rep=rep1&type=pdf

[10] Mcgregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004). Flow Clustering Using Machine Learning Techniques. 205–214.

[11] Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering, 06(05), 551–560. https://doi.org/10.4236/jbise.2013.65070

[12] Ordoñez-Salinas, S., & Gelbukh, A. (2010). Information retrieval with a simplified conceptual graph-like representation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6437 LNAI(PART 1), 92–104. https://doi.org/10.1007/978-3-642-16761-4_9

[13] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2012). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://doi.org/10.1007/s13398-014-0173-7.2

[14] Ren, Y. (2021). Python Machine Learning:Machine Learning and Deep Learning With Python. International Journal of Knowledge-Based Organizations, 11(1), 67–70.

[15] Sebastiani, F. (2001). Machine Learning in Automated Text Categorization. https://doi.org/10.1145/505282.505283

[16] Sidorov, G., Miranda-Jiménez, S., Viveros-Jiménez, F., Gelbukh, A., Castro-Sánchez, N., Velásquez, F., Díaz-Rangel, I., Suárez-Guerra, S., Treviño, A., & Gordon, J. (2013). Empirical study of machine learning based approach for opinion mining in tweets. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7629 LNAI(PART 1), 1–14. https://doi.org/10.1007/978-3-642-37807-2_1

[17] Tanuja, S., Acharya, D., & Shailesh, K. R. (2011). Comparison of different data mining techniques to predict hospital length of stay. Journal of Pharmaceutical and Biomedical Sciences, 07(07).

[18] Trstenjak, B., Mikac, S., & Donko, D. (2014). KNN with TF-IDF based framework for text categorization. Procedia Engineering, 69, 1356–1364. https://doi.org/10.1016/j.proeng.2014.03.129

[19] Verma, V. K., Ranjan, M., & Mishra, P. (2015). Text Mining and Information Professionals.

[20] Wyner, A., & Peters, W. (2010). Lexical semantics and expert legal knowledge towards the identification of legal case factors. Frontiers in Artificial Intelligence and Applications, 223, 127–136. https://doi.org/10.3233/978-1-60750-682-9-127

[21] Zainuddin, N., & Selamat, A. (2014). Sentiment analysis using Support Vector Machine. I4CT 2014 - 1st International Conference on Computer, Communications, and Control Technology, Proceedings, May 2016, 333–337. https://doi.org/10.1109/I4CT.2014.6914200

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