Dilemmas of Prediction Market, Cox Hazard Proportion and Artificial Neural Network Models in Medical Forecasting : Evidence from Chinese Antecedents

Authors

  • Henry Asante Antwi  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
  • Zhou Lulin   School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
  • Mary Opokua Ansong  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and Department of Computer Science, School of Applied Science, Kumasi Polytechnic, P. O. Box 854, Kumasi, Ghana
  • Patrick Acheampong  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China
  • Tehzeeb Mustafa  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
  • Zinet Abdullai  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China

Keywords:

Cox Hazard, Neural Networks, Prognosis, Clinical Judgment, China

Abstract

The conventional technique for forecasting patient survivability is the Cox hazard proportion model, but absolute reliance on this statistical method has its limitations. The emergence of artificial neural networks and prediction markets as swarm intelligence tools has significantly improved medical forecasting in many fronts. Till date the main clinical use of artificial neural networks and prediction markets includes diagnosis, prognosis, disease severity testing and scoring, and measuring the spread of diseases and many other areas of applications yet not many studies have emerged from Jiangsu Province in China. We modeled and compared the historical records and doctor’s evaluation summary of a cohort of 150 patients diagnosed and operated on gastric cancer in general practice using an enhanced prediction market, Cox hazard proportion and artificial neural network. We conclude that prediction market techniques can complement artificial neural networks and Cox hazard regression models in prognosis

References

  1. Akins, Ralitsa B, Tolson, Homer, & Cole, Bryan R. (2005). Stability of response characteristics of a Delphi panel: application of bootstrap data expansion. BMC Medical Research Methodology, 5(1), 1.
  2. Amiri, Zohreh, Mohammad, Kazem, Mahmoudi, Mahmood, Parsaeian, Mahbubeh, & Zeraati, Hojjat. (2013). Assessing the Effect of Quantitative and Qualitative Predictors on Gastric Cancer Individuals Survival Using Hierarchical Artificial Neural Network Models. Iranian Red Crescent Medical Journal, 15(1), 42.
  3. Bollschweiler, Elfriede H, Mönig, Stefan P, Hensler, Karin, Baldus, Stephan E, Maruyama, Keiichi, & Hölscher, Arnulf H. (2004). Artificial neural network for prediction of lymph node metastases in gastric cancer: a phase II diagnostic study. Annals of surgical oncology, 11(5), 506-511.
  4. Chang, Rene WS, Lee, Berni, Jacobs, Sydney, & Lee, Brian. (1989). Accuracy of decisions to withdraw therapy in critically ill patients: Clinical judgment versus a computer model. Critical care medicine, 17(11), 1091-1097.
  5. Colak, M Cengiz, Colak, Cemil, Kocatürk, H, Sa?iro?lu, S, & Barutçu, Irfan. (2008). Predicting coronary artery disease using different artificial neural network models. Anadolu kardiyoloji dergisi: AKD= the Anatolian journal of cardiology, 8(4), 249-254.
  7. Diaz-Aviles, Ernesto, Stewart, Avaré, Velasco, Edward, Denecke, Kerstin, & Nejdl, Wolfgang. (2012). Epidemic Intelligence for the Crowd, by the Crowd. Paper presented at the ICWSM.
  8. Groothuis S, Hasman A, van Pol PEJ, Lencer NHMK, Janssen JJJ, Jans JDMJ, Stappers JLM, Dassen WRM, Doevendans PAFM, van Merode GG (2014) Predicting capacities required in cardiology units for heart failure patients via simulation. Comput Methods Programs Biomed. 2014, 74 (2): 129-141.
  9. Hudak, Ronald P, Brooke, Paul P, & Finstuen, Kenn. (1994). FORECAST 2000: a prediction of skills, knowledge, and abilities required by senior medical treatment facility leaders into the 21st century. Military medicine.
  10. Jerez-Aragonés, José M, Gómez-Ruiz, José A, Ramos-Jiménez, Gonzalo, Muñoz-Pérez, José, & Alba-Conejo, Emilio. (2003). A combined neural network and decision trees model for prognosis of breast cancer relapse. Artificial intelligence in medicine, 27(1), 45-63.
  11. Lam, L. T. and L. Yang (2014). "Is low health literacy associated with overweight and obesity in adolescents: an epidemiology study in a 12-16 years old population, Nanning, China, 2012." Arch Public Health 72(1): 11.
  12. Liu, Xun, Li, Ning-shan, Lv, Lin-sheng, Huang, Jian-hua, Tang, Hua, Chen, Jin-xia, . . . Lou, Tan-qi. (2013). A comparison of the performances of an artificial neural network and a regression model for GFR estimation. American Journal of Kidney Diseases, 62(6), 1109-1115.
  13. Peng, S?Y, & Peng, S? (2008). Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks. Anaesthesia, 63(7), 705-713.
  14. Polgreen, Philip M, Nelson, Forrest D, Neumann, George R, & Weinstein, Robert A. (2007). Use of prediction markets to forecast infectious disease activity. Clinical Infectious Diseases, 44(2), 272-279.
  15. Sato, Fumiaki, Shimada, Yutaka, Selaru, Florin M, Shibata, David, Maeda, Masato, Watanabe, Go, . . . Meltzer, Stephen J. (2005). Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer, 103(8), 1596-1605.
  16. Shi, Hon-Yi, Lee, King-Teh, Lee, Hao-Hsien, Ho, Wen-Hsien, Sun, Ding-Ping, Wang, Jhi-Joung, & Chiu, Chong-Chi. (2012). Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery.
  17. Xu, Yan, Selaru, Florin M, Yin, Jing, Zou, Tong Tong, Shustova, Valentina, Mori, Yuriko, . . . Wang, Suna. (2002). Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett’s esophagus and esophageal cancer. Cancer Research, 62(12), 3493-3497.
  18. Yeh JY, Lin WS (2017) Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department. Expert Systems with Applications 2017;32(4):1073-1083
  19. Zhu, Lucheng, Luo, Wenhua, Su, Meng, Wei, Hangping, Wei, Juan, Zhang, Xuebang, & Zou, Changlin. (2013). Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients. Biomedical reports, 1(5), 757-760.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2017-02-28

Issue

Volume 2, Issue 1, January-February-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Henry Asante Antwi, Zhou Lulin , Mary Opokua Ansong, Patrick Acheampong, Tehzeeb Mustafa, Zinet Abdullai, " Dilemmas of Prediction Market, Cox Hazard Proportion and Artificial Neural Network Models in Medical Forecasting : Evidence from Chinese Antecedents, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 1, pp.119-127, January-February-2017.