La minería de datos en apoyo a la toma de decisiones clínicas

Yidier  Romero Zaldivar; José Felipe Ramírez Pérez; Lissette Soto Pelegrín

Authors

Yidier Romero Zaldivar Universidad de las Ciencias Informáticas
José Felipe Ramírez Pérez Universidad Autónoma de Baja California
Lissette Soto Pelegrín Universidad de las Ciencias Informáticas

Keywords:

data mining, predictive models, clinical information systems

Abstract

Data mining techniques are a tool to keep in mind when a predictive analysis is needed. In the area
of clinical medicine, these predictive data mining techniques are applied to support decision
making by physicians in the diagnosis of diseases, for the prognosis of patient survival and to
suggest treatments. The authors of this paper set out to conduct a literature review to identify trends
in the subject, the most precise techniques in the task of prediction and its application in clinical
medicine. A method of systematic literature review (SLR) was applied to comply with the proposed
objective. At the end of the work, three important criteria were identified to choose an effective
model for predictive analysis in clinical data: the representation of the problem, the explanatory
power of its exit and the ability to add previous knowledge of the experts in the domain.
Keywords: data mining; predictive models; clinical information systems

References

AbuKhousa, E., & Campbell, P. (2012). Predictive data mining to support clinical decisions: An overview of heart disease prediction systems. 2012 International Conference on Innovations in Information Technology (IIT), 267-272. Obtenido de https://doi.org/10.1109/INNOVATIONS.2012.6207745

Afeni, B. O., Aruleba, T. I., & Oloyede, I. A. (2017). Hypertension Prediction System Using Naive Bayes Classifier. Journal of Advances in Mathematics and Computer Science, 1-11. Obtenido de https://doi.org/10.9734/JAMCS/2017/35610

Bielza, C., & Larrañaga, P. (2014). Discrete Bayesian Network Classifiers: A Survey. ACM Computing Surveys, 47(1), 5:1-5:43. Obtenido de https://doi.org/10.1145/2576868

Biran, O., & Cotton, C. (2017). Explanation and justification in machine learning: A survey.

IJCAI-17 workshop on explainable AI (XAI), 8(1), 8-13.

Blobel, B. (2017). Knowledge representation and knowledge management as basis for decision support systems. Int J Biomed Healthc, 5, 13-20.

Danjuma, K., & Osofisan, A. O. (2015). Evaluation of predictive data mining algorithms in erythemato-squamous disease diagnosis. arXiv preprint arXiv:1501.00607.

Duda, R. O., & Shortliffe, E. H. (1983). Expert systems research. Science, 220(4594), 261-268. Dutta, P. (2017). Decision Making in Medical Diagnosis via Distance Measures on Interval

Valued Fuzzy Sets. International Journal of System Dynamics Applications (IJSDA), 6(4), 63-83. Obtenido de https://doi.org/10.4018/IJSDA.2017100104

Gupta, S., Kumar, D., & Sharma, A. (2011). Data mining classification techniques applied for breast cancer diagnosis and prognosis. Indian Journal of Computer Science and Engineering (IJCSE), 2(2), 188-195.

Hale, A. T., Stonko, D. P., Lim, J., Guillamondegui, O. D., Shannon, C. N., & Patel, M. B. (2018). Using an artificial neural network to predict traumatic brain injury. Journal of Neurosurgery: Pediatrics, 23(2), 219-226. Obtenido de https://doi.org/10.3171/2018.8.PEDS18370

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Techniques (3rd ed), Morgan Kauffman, 705. https://doi.org/10.1016/C2009-0-61819-5

Hsieh, M.-H., Hsieh, M.-J., Chen, C.-M., Hsieh, C.-C., Chao, C.-M., & Lai, C.-C. (2018). An

Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units. Journal of Clinical Medicine, 7(9), 240. Obtenido de https://doi.org/10.3390/jcm7090240

Hurria, A., Mohile, S., Gajra, A., Klepin, H., Muss, H., Chapman, A., Feng, T., Smith, D., Sun,

C.-L., De Glas, N., Cohen, H. J., Katheria, V., Doan, C., Zavala, L., Levi, A., Akiba, C., & Tew, W. P. (2016). Validation of a Prediction Tool for Chemotherapy Toxicity in Older Adults With Cancer. Journal of Clinical Oncology, 34(20), 2366-2371. Obtenido de https://doi.org/10.1200/JCO.2015.65.4327

Iavindrasana, J., Cohen, G., Depeursinge, A., Müller, H., Meyer, R., & Geissbuhler, A. (2009).

Clinical data mining: A review. Yearbook of medical informatics, 18(01), 121-133.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.

Krypotos, A.-M., Blanken, T. F., Arnaudova, I., Matzke, D., & Beckers, T. (2017). A Primer on Bayesian Analysis for Experimental Psychopathologists. Journal of Experimental Psychopathology, 8(2), 140-157. Obtenido de https://doi.org/10.5127/jep.057316

Mishra, M., & Srivastava, M. (2014). A view of Artificial Neural Network. 2014 International Conference on Advances in Engineering Technology Research (ICAETR - 2014), 1-3. https://doi.org/10.1109/ICAETR.2014.7012785

Momenyan, S., Baghestani, A. R., Momenyan, N., Naseri, P., & Akbari, M. E. (2018). Survival prediction of patients with breast cancer: Comparisons of decision tree and logistic regression analysis. International Journal of Cancer Management, In Press.

Phakhounthong, K., Chaovalit, P., Jittamala, P., Blacksell, S. D., Carter, M. J., Turner, P., Chheng, K., Sona, S., Kumar, V., Day, N. P. J., White, L. J., & Pan-ngum, W. (2018). Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: Application of classification tree analysis. BMC Pediatrics, 18(1), 109. https://doi.org/10.1186/s12887-018-1078-y

Pourhoseingholi, M. A., Kheirian, S., & Zali, M. R. (2017). Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients. Acta Informatica Medica, 25(4), 254.

Rabbi, M., Hane Aung, M., & Choudhury, T. (2017). Towards Health Recommendation Systems: An Approach for Providing Automated Personalized Health Feedback from Mobile Data. En J. M. Rehg, S. A. Murphy, & S. Kumar (Eds.), Mobile Health: Sensors, Analytic Methods, and Applications (pp. 519-542). Springer International Publishing. https://doi.org/10.1007/978-3-319-51394-2_26

Saranya, P., & Satheeskumar, B. (2016). A Survey on Feature Selection of Cancer Disease Using Data Mining Techniques. International Journal of Computer Science and Mobile Computing, 5(5), 713-719.

Singh, S., & Gupta, P. (2014, julio). Comparative Study ID3, Cart and C4.5 Decision Tree Algorithm: A Survey. International Journal of Advanced Information Science and Technology (IJAIST), 27(27).

Torres-Carrión, P. V., González-González, C. S., Aciar, S., & Rodríguez-Morales, G. (2018).

Methodology for systematic literature review applied to engineering and education. 2018 IEEE Global Engineering Education Conference (EDUCON), 1364-1373. https://doi.org/10.1109/EDUCON.2018.8363388

Wang, H., Zheng, B., Yoon, S. W., & Ko, H. S. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, 267(2), 687-699. https://doi.org/10.1016/j.ejor.2017.12.001

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data Mining. Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.

Xiao, Y., & Watson, M. (2017). Guidance on Conducting a Systematic Literature Review.

Journal of Planning Education and Research, 0739456X17723971. https://doi.org/10.1177/0739456x17723971

Yang, F.-J. (2018). An Implementation of Naive Bayes Classifier. 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 301-306. https://doi.org/10.1109/CSCI46756.2018.00065

Ye, N. (2014). Data Mining. Theories, Algorithms, and Examples. CRC Press.

Zhang, Y., Guo, S.-L., Han, L.-N., & Li, T.-L. (2016). Application and Exploration of Big Data Mining in Clinical Medicine. Chinese Medical Journal, 129(6), 731-738. https://doi.org/10.4103/0366-6999.178019

Zhang, Z. (2016). Introduction to machine learning: K-nearest neighbors. Annals of Translational Medicine, 4(11), 218. https://doi.org/10.21037/atm.2016.03.37