Journal of Computing Science and Engineering

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Use of Information Technologies to Explore Correlations between Climatic Factors and Spontaneous Intracerebral Hemorrhage in Different Age Groups

  • Ting, Hsien-Wei (Department of Neurosurgery, Taipei Hospital, Ministry of Health and Welfare) ;
  • Chan, Chien-Lung (Department of Information Management, Yuan Ze University) ;
  • Pan, Ren-Hao (Department of Computer Science and Engineering, Yuan Ze University) ;
  • Lai, Robert K. (Department of Computer Science and Engineering, Yuan Ze University) ;
  • Chien, Ting-Ying (Department of Computer Science and Engineering, Yuan Ze University)
  • Received : 2017.05.25
  • Accepted : 2017.11.25
  • Published : 2017.12.30
⟨ Previous Next ⟩

Abstract

Spontaneous intracerebral hemorrhage (sICH) has a high mortality rate. Research has demonstrated that sICH occurrence is related to weather conditions; therefore, this study used the decision tree method to explore the impact of climatic risk factors on sICH at different ages. The Taiwan National Health Insurance Research Database (NHIRD) and other open-access data were used in this study. The inclusion criterion was a first-attack sICH. The decision tree algorithm and random forest were implemented in R programming language. We defined a high risk of sICH as more than the average number of cases daily, and the younger, middle-aged and older groups were calculated as having 0.77, 2.26 and 2.60 cases per day, respectively. In total, 22,684 sICH cases were included in this study; 3,102 patients were younger (<44 years, younger group), 9,089 were middle-aged (45-64 years, middle group), and 10,457 were older (>65 years, older group). The risk of sICH in the younger group was not correlated with temperature, wind speed or humidity. The middle group had two decision nodes: a higher risk if the maximum temperature was >$19^{\circ}C$ (probability = 63.7%), and if the maximum temperature was <$19^{\circ}C$ in addition to a wind speed <2.788 (m/s) (probability = 60.9%). The older group had a higher risk if the average temperature was >$23.933^{\circ}C$ (probability = 60.7%). This study demonstrated that the sICH incidence in the younger patients was not significantly correlated with weather factors; that in the middle-aged sICH patients was highly-correlated with the apparent temperature; and that in the older sICH patients was highly-correlated with the mean ambient temperature. "Warm" cold ambient temperatures resulted in a higher risk of sICH, especially in the older patients.

Keywords

Acknowledgement

Supported by : Ministry of Science and Technology

References

  1. C. Chan, H. Ting, and H. Huang, "The incidence, hospital expenditure, and, 30day and 1year mortality rates of spontaneous intracerebral hemorrhage in Taiwan," Journal of Clinical Neurosciene, vol. 21, no. 1, pp. 91-94, 2013.
  2. C. Chan, H. Ting, and H. Huang, "The definition of a prolonged intensive care unit stay for spontaneous intracerebral hemorrhage patients: an application with national health insurance research database," BioMed Research International, vol. 2014, article no. 891725, 2014.
  3. M. H. Han, H. J. Yi, Y. S. Kim, and Y. S. Kim, "Effect of seasonal and monthly variation in weather and air pollution factors on stroke incidence in Seoul, Korea," Stroke, vol. 46, no. 4, pp. 927-935, 2015. https://doi.org/10.1161/STROKEAHA.114.007950
  4. T. S. Jeong, C. W. Park, C. J. Yoo, E. Y. Kim, Y. B. Kim, and W. K. Kim, "Association between the daily temperature range and occurrence of spontaneous intracerebral hemorrhage," Journal of Cerebrovascular and Endovascular Neurosurgery, vol. 15, no. 3, pp. 152-157, 2013. https://doi.org/10.7461/jcen.2013.15.3.152
  5. H. J. Kim, J. H. Kim, D. R. Kim, H. I. Kang, B. G. Moon, and J. S. Kim, "Age and meteorological factors in the occurrence of spontaneous intracerebral hemorrhage in a metropolitan city," Journal of Cerebrovascular and Endovascular Neurosurgery, vol. 16, no. 3, pp. 209-215. 2014. https://doi.org/10.7461/jcen.2014.16.3.209
  6. A. Zeka, S. Browne, H. McAvoy, and P. Goodman, "The association of cold weather and all-cause and cause-specific mortality in the island of Ireland between 1984 and 2007," Environmental Health, vol. 13, article no. 104, 2014.
  7. J. Kwon, H. S. Park, S. H. Kim, and K. S. Lee, "Impacts of gender, weather, and workplace differences in farm worker's gear," Journal of Physiological Anthropology, vol. 34, article no. 39, 2015.
  8. C. L. Chan and H. Ting, "Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm," Expert Systems with Applications, vol. 38, no. 7, pp. 7924-7928, 2011. https://doi.org/10.1016/j.eswa.2010.10.094
  9. I. Kamkar, S. K. Gupta, D. Phung, and S. Venkatesh, "Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso," Journal of Biomedical Informatics, vol. 53, pp. 277-290, 2015. https://doi.org/10.1016/j.jbi.2014.11.013
  10. A. Khosla, Y. Cao, C. C. Y. Lin, H. K. Chiu, J. Hu, and H. Lee, "An integrated machine learning approach to stroke prediction," in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, 2010, pp. 183-192.
  11. H. W. Ting, J. T. Wu, C. L. Chan, S. L. Lin, and M. H. Chen, "Decision model for acute appendicitis treatment with decision tree technology-a modification of the alvarado scoring system," Journal of Chines Medical Association, vol. 73, no. 8, pp. 401-406, 2010. https://doi.org/10.1016/S1726-4901(10)70087-3
  12. C. F. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, and X. D. Koutsoukos, "Local causal and markov blanket induction for causal discovery and feature selection for classification. Part I: Algorithms and empirical evaluation," Journal of Machine Learning Research, vol. 11, pp. 171-234, 2010.
  13. M. R. Baneshi, A. A. Haghdoost, F. Zolala, N. Nakhaee, M. Jalali, R. Tabrizi, and M. Akbari, "Can religious beliefs be a protective factor for suicidal behavior? A decision tree analysis in a mid-sized city in Iran, 2013," Journal of Religion and Health, vol. 56, no. 2, pp. 428-436, 2017. https://doi.org/10.1007/s10943-016-0215-x
  14. C. L. Chan, W. Lin, N. P. Yang, and H. T. Huang, "The association between the availability of ambulatory care and non-emergency treatment in emergency medicine departments: a comprehensive and nationwide validation," Health Policy, vol. 110, no. 2, pp. 271-279, 2013. https://doi.org/10.1016/j.healthpol.2012.12.003
  15. K. B. Lin, K. R. Lai, N. P. Yang, K. S. Wu, H. W. Ting, R. H. Pan, and C. L. Chan, "Trends and outcomes in the utilization of laparoscopic appendectomies in a low-income population in Taiwan from 2003 to 2011," International Journal for Equity in Health, vol. 14, article no. 100, 2015.
  16. Introduction to the National Health Insurance Research Database (NHIRD), https://nhird.nhri.org.tw/en/.
  17. Taiwan Government Open Data Platform, http://data.gov.tw/.
  18. R. Ihaka and R. Gentleman, "R: a language for data analysis and graphics," Journal of Computational and Graphical Statistics, vol. 5, no. 3, pp. 299-314, 1996. https://doi.org/10.2307/1390807
  19. Request Research, "Data Mining Tools See5 and C5.0," 2017; http://www.rulequest.com/see5-info.html.
  20. J. R. Quinlan, "Improved use of continuous attributes in C4. 5," Journal of Artificial Intelligence Research, vol. 4, pp. 77-90, 1996. https://doi.org/10.1613/jair.279
  21. M. Kuhn, S. Weston, N. Coulter, and M. M. Kuhn, "Package 'C50'," 2017; https://cran.r-project.org/web/packages/C50/C50.pdf.
  22. R. Pandya and J. Pandya, "C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning," International Journal of Computer Applications, vol. 117, no. 16, pp. 18-21, 2015. https://doi.org/10.5120/20639-3318
  23. J. R. Quinlan, "Induction of decision trees," Machine Learning, vol. 1, no. 1, pp. 81-106, 1986. https://doi.org/10.1007/BF00116251
  24. T. M. Therneau, B. Atkinson, and M. B. Ripley, "Package 'rpart'," 2017; https://cran.r-project.org/web/packages/rpart/rpart.pdf.
  25. L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Boca Raton, FL: Chapman & Hall/CRC, 1984.
  26. A. Liaw and M. Wiener, "Classification and regression by RandomForest," R News, vol. 2, no. 3, pp. 18-22, 2002.
  27. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324
  28. C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, "Bias in random forest variable importance measures: illustrations, sources and a solution," BMC Bioinformatics, vol. 8, article no. 25, 2007.
  29. D. Zheng, H. Arima, E. Heeley, A. Karpin, J. Yang, J. Chalmers, and C. S. Anderson, "Ambient temperature and volume of perihematomal edema in acute intracerebral haemorrhage: the INTERACT1 study," International Journal of Stroke, vol. 10, no. 1, pp. 25-27, 2015. https://doi.org/10.1111/ijs.12389
  30. D. Zheng, H. Arima, S. Sato, A. Gasparrini, E. Heeley, C. Delcourt, S. Lo, Y. Huang, J. Wang, C. Stapf, et al. "Low ambient temperature and intracerebral hemorrhage: the INTERACT2 study," PLoS One, vol. 11, no. 2, article no. e0149040, 2016. https://doi.org/10.1371/journal.pone.0149040
  31. R. M. Garland, M. Matooane, F. A. Engelbrecht, M. J. M. Bopape, W. A. Landman, M. Naidoo, J. van der Merwe, and C. Y. Wright, et al., "Regional projections of extreme apparent temperature days in africa and the related potential risk to human health," International Journal of Environmental Research and Public Health, vol. 12, no. 10, pp. 12577-12604, 2015. https://doi.org/10.3390/ijerph121012577
  32. H. C. Ho, A. Knudby, Y. Xu, M. Hodul, and M. Aminipouri, "A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area," Science of the Total Environment, vol. 544, pp. 929-938, 2016. https://doi.org/10.1016/j.scitotenv.2015.12.021
  33. S. L. Lai, S. T. Chen, T. H. Lee, L. S. Ro, and S. P. Hsu, "Spontaneous intracerebral hemorrhage in young adults," European Journal of Neurology, vol. 12, no. 4, pp. 310-316, 2005. https://doi.org/10.1111/j.1468-1331.2004.00957.x
  34. T. M. O'Lynnger, W. N. Al-Holou, J. J. Gemmete, A. S. Pandey, B. G. Thompson, H. J. Garton, and C. O. Maher, "The effect of age on arteriovenous malformations in children and young adults undergoing magnetic resonance imaging," Child's Nervous System, vol. 27, no. 8, pp. 1273-1279, 2011. https://doi.org/10.1007/s00381-011-1434-9