Computerized Systems Supporting Clinical Decision in Medicine-Reference-Cited by-同舟云学术

Computerized Systems Supporting Clinical Decision in Medicine

Published:2018-12-01 Issue:1 Volume:56 Page:107-120
ISSN:2199-6059
Container-title:Studies in Logic, Grammar and Rhetoric
language:en
Short-container-title:

Author:

Owczarek Aleksander J.¹,Smertka Mike²,Jędrusik Przemysław³,Gębska-Kuczerowska Anita⁴,Chudek Jerzy⁵,Wojnicz Romuald⁶

Affiliation:

1. Department of Statistics, Department of Instrumental Analysis , School of Pharmacy with the Division of Laboratory Medicine in Sosnowiec , Medical University of Silesia in Katowice , Poland

2. Pathophysiology Unit, Department of Pathophysiology , School of Medicine in Katowice , Medical University of Silesia in Katowice , Poland

3. Department of Computer Biomedical Systems, Institute of Computer Science , University of Silesia , Poland

4. National Institute of Public Health, National Institute of Hygiene in Warsaw , Poland

5. Department of Internal Medicine and Oncological Chemotherapy, Medical Faculty in Katowice , Medical University of Silesia in Katowice , Poland

6. Department of Histology and Embryology , School of Medicine with the Division of Dentistry in Zabrze , Medical University of Silesia in Katowice , Poland

Abstract

Abstract Statistics is the science of collection, summarizing, presentation and interpretation of data. Moreover, it yields methods used in the verification of research hypotheses. The presence of a statistician in a research group remarkably improves both the quality of design and research and the optimization of financial resources. Moreover, the involvement of a statistician in a research team helps the physician to effectively utilize the time and energy spent on diagnosing, which is an important aspect in view of limited healthcare resources. Precise, properly designed and implemented Computerized Clinical Decision Support Systems certainly lead to the improvement of healthcare and the quality of medical services, which increases patient satisfaction and reduces financial burdens on healthcare systems.

Publisher

Walter de Gruyter GmbH

Subject

Philosophy

Link

https://www.sciendo.com/pdf/10.2478/slgr-2018-0044

Reference69 articles.

1. Abdala, O., & Saeed, M. (2004). Estimation of Missing Values in Clinical Laboratory Measurements of ICU Patients Using a Weighted K-Nearest Neighbors Algorithm. Computers in Cardiology, 31, 693–696.

2. Adams, K., Uddin, N., & Patterson, J. (2008). Clinical predictors of in-hospital mortality in acutely decompensated heart failure-piecing together the outcome puzzle. Congestive Heart Failure, 14(3), 127–134.

3. Ajith, A. (2005). Artificial Neural Networks. In P. H. Sydenham & R. Thorn (Eds.), Handbook for Measurement Systems Design (pp. 901–908). London: John Wiley and Sons Ltd.

4. Arif, M., Akram, M., & Minhas, F. (2010). Pruned fuzzy K-nearest neighbor classifier for beat classification. Journal of Biomedical Science and Engineering, 3(4), 380–389.10.4236/jbise.2010.34053

5. Asi, B., Setarehdan, S., & Mohebbi, M. (2008). Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine, 44(1), 51–64.10.1016/j.artmed.2008.04.007

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