Clinical Decision Support Systems

Authors

  • Pratibha Tiwari  Department of Computer Science and Engineering Parul University, Limda, Waghodia Gujarat, India
  • Nishant Shah  Department of Computer Science and Engineering Parul University, Limda, Waghodia Gujarat, India
  • Evanglelin Samuel  Department of Computer Science and Engineering Parul University, Limda, Waghodia Gujarat, India
  • Poojan Shah  Department of Computer Science and Engineering Parul University, Limda, Waghodia Gujarat, India
  • Yask Patel  Department of Computer Science and Engineering Parul University, Limda, Waghodia Gujarat, India
  • Pratibha Tiwari  Department of Computer Science and Engineering Parul University, Limda, Waghodia Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT1952264

Keywords:

Clinical Decision Support System, Decision Support System, Disease Prediction, Machine Learning.

Abstract

Nowadays, every field is digitizing their data for easy access at anytime and anywhere or even for enclosed cabinet servers, especially the health care sector. But, that is not the only reason health care sector is computerizing its data. These huge chucks of records are used for research purposes. Many hospitals are working with education institutes with research departments (Damian Borbolla et.al 2010).CDSS performs Knowledge-based analyses on these EHRs and running disease prediction models on these data is done. There may be many complications. We have reviewed the problems faced by such system from previous researches and implemented systems.

References

  1. Wright, A. and Sittig, D. (2008). A four-phase model of the evolution of clinical decision support architectures. International Journal of Medical Informatics, 77(10), pp.641-649.
  2. Kaushal, R., Shojania, K. and Bates, D. (2003). Effects of Computerized Physician Order Entry and Clinical Decision Support Systems on Medication Safety. Archives of Internal Medicine, 163(12), p.1409.
  3. Hwang, U., Choi, S., Lee, H. and Yoon, S. (2019). Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data. [online] arXiv.org. Avail- able at: https://arxiv.org/abs/1711.04126 [Accessed 26 Feb. 2019].
  4. Chen, Y., Lin, C., Wang, K., Rahman, L., Lee, D., Chung, W. and Lin, H. (2018). Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset.Journal of Healthcare Engineering, 2018, pp.1-13.
  5. Kawamoto, K., Houlihan, C., Balas, E. and Lobach, D. (2019). Improving clinical prac- tice using clinical decision support systems: a systematic review of trials to identify features critical to success.
  6. Gaser J, Teich JM, Kupermsn G.Impact of Information events on medical care. In: Proceedings on the 1996 IMMS Annual Conference. Chicago, III: Healthcare Information and Management Systems Society; 1996:1-9.
  7. Borbolla, Damian Otero, Carlos Lobach, David Kawamoto, Kensaku M Gomez Saldao, Ana Staccia, Gustavo Gastn, Lpez Figar, Silvana Luna, Daniel Quirs, Fernn. (2010). Implementation of a clinical decision support system using a service model: Results of a feasibility study. Studies in health technology and informatics. 160. 816-20. 10.3233/978- 1-60750-588-4-816.

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Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
Pratibha Tiwari, Nishant Shah, Evanglelin Samuel, Poojan Shah, Yask Patel, Pratibha Tiwari, " Clinical Decision Support Systems , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.993-995, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952264