Artificial Neural Networks and Crowd Wisdom Theories in Health Planning : A Comparative Analysis from Eastern China

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
  • Basil Kusi  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China
  • Micheal Owusu Akomeah  School of Management, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, P.R. China

Keywords:

Anti-Guessing, Eviews and JAGS, ANN, AIC, AR

Abstract

Over the year’s hospital management and planning has been shaped by the clinical “anti-guessing” theory of Evidence Based Medicine practiced globally by clinicians. This is because most healthcare facilities are managed by medical officers with significant bias towards exporting their clinical experiences to medical forecasting and decision making for all aspects of healthcare including patient flow, bed allocation, transport scheduling, staff scheduling, supply chain management, menu services. We investigated the extent to which experiences of healthcare professionals can be harnessed and optimized for evidential use as a swarm intelligence  technique. A demand forecasting scenario was modeled based on data collected from healthcare facilities in China. The data was simultaneously modeled using artificial neural networks and prediction market. The prediction market and artificial neural network models were equally competitive with no statistical difference in mean average percentage error. Both of them outperformed the unweighted linear average method. 

References

  1. Al-Qahtani FH, Crone SF (2013) Multivariate k-nearest neighbour regression for time series data—A novel algorithm for forecasting UK electricity demand. 2013 Presented at: Neural Networks (IJCNN), The 2013 International Joint Conference; August 4-9, 2013; Dallas, TX.
  2. Altman NS (2012) An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 2012;46(3):175-185.
  3. 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.
  4. Bassi, PierFrancesco, Sacco, Emilio, De Marco, Vincenzo, Aragona, Maurizio, & Volpe, Andrea. (2007). Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis. BJU international, 99(5), 1007-1012.
  5. Batchelor, Roy, and Pami Dua ((2015). Forecaster Diversity and the Benefits of Combining Forecasts. Management Science, 41, 68-75.
  6. Chen, Long-jian, Lian, Guo-ping, & Han, Lu-jia. (2007). Prediction of human skin permeability using artificial neural network (ANN) modeling. Acta Pharmacologica Sinica, 28(4), 591.
  7. Mushlin, Alvin I, & Appel, Francis A. (1977). Diagnosing potential noncompliance: physicians' ability in a behavioral dimension of medical care. Archives of Internal Medicine, 137(3), 318-321.
  8. Nimgaonkar, A, & Sudarshan, S. (2004). Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models. Intensive Care Med, 30, 248-253.
  9. Rajakovich, David, & Vladimirov, Vladimir. (2009). Prediction markets as a medical forecasting tool: Demand for hospital services. Journal of Prediction Markets, 3(2), 78-106.
  10. Sporer, Karl A, Craig, Alan M, Johnson, Nicholas J, & Yeh, Clement C. (2010). Does emergency medical dispatch priority predict delphi process-derived levels of prehospital intervention? Prehospital and disaster medicine, 25(04), 309-317.
  11. Starkweather, David B, Gelwicks, Louis, & Newcomer, Robert. (1975). Delphi forecasting of health care organization. Inquiry, 12(1), 37-46.
  12. Sterk WE, Shryock EG (2007) Modern methods improve hospital forecasting. Healthc Financ Manage. 2007, 41: 96-8.PubMed
  13. Wei, Long, Zewei, Zhou, Hao, Zhang, Guangyun, Yu, Ming, Jiang, Jiali, Bai, . . . Peixun, Liu. (2013). Construction of A Prediction System for Chinese Materia Medica Hot/Cold Properties J]. World Chinese Medicine, 3, 033.
  14. Weinstein, Neil D, & Klein, William M. (1996). Unrealistic optimism: Present and future. Journal of Social and Clinical Psychology, 15(1), 1-8.
  15. Wilson, Andrew M, Thabane, Lehana, & Holbrook, Anne. (2004). Application of data mining techniques in pharmacovigilance. British journal of clinical pharmacology, 57(2), 127-134.

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

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Published

2017-02-28

Issue

Volume 2, Issue 1, January-February-2017

Section

Research Articles

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How to Cite

[1]
Henry Asante Antwi, Zhou Lulin, Mary Opokua Ansong, Basil Kusi, Micheal Owusu Akomeah, " Artificial Neural Networks and Crowd Wisdom Theories in Health Planning : A Comparative Analysis from Eastern China, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 1, pp.128-134 , January-February-2017.