Health Checkup could Reveal Chronic Disorders with Support from Artificial Intelligence

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2019 by IJETT Journal
Volume-67 Issue-11
Year of Publication : 2019
Authors : Shuangquan Li, Tongbin Zhang, Chuandi Pan, Li Cai
  10.14445/22315381/IJETT-V67I11P202

MLA 

MLA Style: Shuangquan Li, Tongbin Zhang, Chuandi Pan, Li Cai  "Health Checkup could Reveal Chronic Disorders with Support from Artificial Intelligence" International Journal of Engineering Trends and Technology 67.11 (2019):8-15.

APA Style: Shuangquan Li, Tongbin Zhang, Chuandi Pan, Li Cai. Health Checkup could Reveal Chronic Disorders with Support from Artificial Intelligence  International Journal of Engineering Trends and Technology, 67(11),8-15.

Abstract
After decades of practice, healthcare specialists have not reached the conclusion: to what extent health checkup could improve the quality of care. In this paper, we join this debate with a larger health checkup cohort than most of the previous studies. In addition, we examine the health checkup potential in a new task: identifying chronic diseases for the individual with the support of digital health (Health IT) and Artificial Intelligence (AI). Our results show that with the assistance of Health IT and AI, the health checkup data could identify many types of chronic disorder with high precision. In addition, we found specific associations between occurrence of chronic disease and results of lab tests in the health checkup. Using these associations not only improves the predictive performance but also points out that the health checkup could have a role in preventive care. Furthermore, the results provide some evidence for the healthcare organizations to design a better and cheaper checkup service, which uses a smaller number of tests but preserves a similar predictive capacity. Therefore, the health checkup, with support from Health IT and AI, has rich potential to improve the quality of care in predictive and preventive tasks.

Reference

[1] Krogsboll, L. T., K. J. Jorgensen, C. Gronhoj Larsen and P. C. Gotzsche (2012). "General health checks in adults for reducing morbidity and mortality from disease." Cochrane Database Syst Rev10: CD009009.
[2] Si, S., J. R. Moss, T. R. Sullivan, S. S. Newton and N. P. Stocks (2014). "Effectiveness of general practice-based health checks: a systematic review and meta-analysis." Br J Gen Pract64(618): e47-53.
[3] Suh, Y., C. J. Lee, D. K. Cho, Y. H. Cho, D. H. Shin, C. M. Ahn, J. S. Kim, B. K. Kim, Y. G. Ko, D. Choi, Y. Jang and M. K. Hong (2017). "Impact of National Health Checkup Service on Hard Atherosclerotic Cardiovascular Disease Events and All-Cause Mortality in the General Population." Am J Cardiol120(10): 1804-1812.
[4] Kudo, Y., T. Satoh, S. Kido, M. Ishibashi, E. Miyajima, M. Watanabe, T. Miki, M. Tsunoda and Y. Aizawa (2008). "The degree of workers' use of annual health checkup results among Japanese workers." Ind Health46(3): 223-232.
[5] Cao, X., J. Zhou, H. Yuan and Z. Chen (2015). "Cumulative effect of reproductive factors on ideal cardiovascular health in postmenopausal women: a cross-sectional study in central south China." BMC Cardiovasc Disord15: 176.
[6] Gu, D., P. Xu, Y. Yuan and H. Fu (2016). "Albuminuria is Suggested as a Potential Health Screening Biomarker for Senior Citizens and General Population with Hypertension or Diabetes in China." Clin Lab62(11): 2267-2269.
[7] Altman, D. G. (1990). Practical statistics for medical research, CRC press.
[8] Vapnik, V., S. E. Golowich and A. J. Smola (1997). Support vector method for function approximation, regression estimation and signal processing. Advances in neural information processing systems.
[9] Kamkar, I., S. K. Gupta, D. Phung and S. Venkatesh (2015). "Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso." J Biomed Inform53: 277-290.
[10] Miotto, R., L. Li, B. A. Kidd and J. T. Dudley (2016). "Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records." Sci Rep6: 26094.
[11] Van Der Maaten, L. and G. Hinton (2017). "Visualizing data using t-sne (2008)." J Mach Learn Res1117(9): 2579-2605.
[12] Weiskopf, N. G., G. Hripcsak, S. Swaminathan and C. Weng (2013). "Defining and measuring completeness of electronic health records for secondary use." J Biomed Inform46(5): 830- 836.
[13] Weiskopf, N. G. and C. Weng (2013). "Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research." J Am Med Inform Assoc20(1): 144-151.
[14] Dahlem, D., D. Maniloff and C. Ratti (2015). "Predictability Bounds of Electronic Health Records." Sci Rep5: 11865.
[15] Liaw, A. and M. Wiener (2002). "Classification and regression by randomForest." R news2(3): 18-22.
[16] "Bioscience research thriving in Wenzhou's Ouhai Life and Health Town."(2016) Retrieved 09/09/2017, from http://subsites.chinadaily.com.cn/ezhejiang/2016- 09/28/c_58228.htm.
[17] ICD-10 online versions, World Health Organization.(2014)
[18] Peck, R., C. Olsen and J. L. Devore (2015). Introduction to statistics and data analysis, Cengage Learning.
[19] Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten (2009). "The WEKA data mining software: an update." ACM SIGKDD explorations newsletter11(1): 10-18.
[20] Vapnik, V., S. E. Golowich and A. J. Smola (1997). Support vector method for function approximation, regression estimation and signal processing. Advances in neural information processing systems.
[21] Cheadle, C., M. P. Vawter, W. J. Freed and K. G. Becker (2003). "Analysis of microarray data using Z score transformation." J Mol Diagn5(2): 73-81.
[22] Martines, J. (2016). Rich Man, Poor Health: Class and Health in Modern China. The Diplomat.
[23] Hong, Q. Y., G. M. Wu, G. S. Qian, C. P. Hu, J. Y. Zhou, L. A. Chen, W. M. Li, S. Y. Li, K. Wang, Q. Wang, X. J. Zhang, J. Li, X. Gong, C. X. Bai, S. Lung Cancer Group of Chinese Thoracic and C. Chinese Alliance Against Lung (2015). "Prevention and management of lung cancer in China." Cancer121 Suppl 17: 3080-3088.
[24] Hirsch, A. G. and A. Scheck McAlearney (2013). "Measuring Diabetes Care Performance Using Electronic Health Record Data: The Impact of Diabetes Definitions on Performance Measure Outcomes." Am J Med Qual29(4): 292-299.
[25] Raghunathan, T. E. (2004). "What do we do with missing data? Some options for analysis of incomplete data." Annu Rev Public Health25: 99-117.

Keywords
Health checkup, Artificial Intelligence, Machine Learning, Health IT, Chronic disease prediction