A fuzzy logic decision support system for assessing clinical nutritional risk

Document Type : Articles

Authors

Abstract

Introduction: Studies have indicated a global high prevalence of hospital malnutrition on admission and during hospitalization. Clinical Nutritional Risk Screen (CNRS) is a way to identify malnutrition and manage nutritional interventions. Several traditional and non-computer based tools have been suggested for screening nutritional risk levels. The present study was an attempt to employ a computer based fuzzy model decision support system as a nutrition-screening tool for inpatients.Method: This is an applied modeling study. The system architecture was designed based on the fuzzy logic model including input data, inference engine, and output. A clinical nutritionist entered nineteen input variables using a windows-based graphical user interface. The inference engine was involved with knowledge obtained from literature and the construction of ‘IF-THEN’ rules. The output of the system was stratification of patients into four risk levels from ‘No’ to ‘High’ where a number was also allocated to them as a nutritional risk grade. All patients (121 people) admitted during implementing the system participated in testing the model. The classification tests were used to measure the CNRS fuzzy model performance. IBM SPSS version 21 was utilized as a tool for data analysis with α = 0.05 as a significance level.Results: Results showed that sensitivity, specificity, accuracy, and precision of the fuzzy model performance were 91.67% (±4.92), 76% (±7.6), 88.43% (±5.7), and 93.62% (±4.32), respectively. Instant performance on admission and very low probability of mistake in predicting malnutrition risk level may justify using the model in hospitals.Conclusion: To conclude, the fuzzy model-screening tool is based on multiple nutritional risk factors, having the capability of classifying inpatients into several nutritional risk levels and identifying the level of required nutritional intervention.Keywords: Clinical decision support system; Fuzzy sets; Intelligent system; Expert system; Nutritional risk assessment

References

  1. Mahan L, Escott-Stump S. Krause’s Food & Nutrition Therapy, International Edition. 12th ed. Canada: Saunders; 2008.
  2. Morris J. Dietitian’s guide to assessment and documentation (Pp9). USA: Jones & Partlett. 2011.
  3. van Bokhorst-de van der Schueren MA, Guaitoli PR, Jansma EP, de Vet HC. Nutrition screening tools: Does one size fit all? A systematic review of screening tools for the hospital setting. Clinical nutrition. 2014;33(1):39-58.
  4. Vellas B, Sieber C. The MNA® revisited: what does the data tell us? 1994.
  5. Cant R. Investing in patients’ nutrition: nutrition risk screening in hospital. Aust J Adv Nurs. 2011;28(2):81-7.
  6. Cawood AL, Elia M, Sharp SK, Stratton RJ. Malnutrition self-screening by using MUST in hospital outpatients: validity, reliability, and ease of use. The American journal of clinical nutrition. 2012;96(5):1000-7.
  7. Mirmiran P, Hosseinpour-Niazi S, Mehrabani HH, Kavian F, Azizi F. Validity and reliability of a nutrition screening tool in hospitalized patients. Nutrition. [Research Support, Non-U.S. Gov’t Validation Studies]. 2011;27(6):647-52.
  8. Rasmussen HH, Holst M, Kondrup J. Measuring nutritional risk in hospitals. Clinical epidemiology. 2010;2:209-16.
  9. Hadianfard A, Abdul-Kareem S, Karandish M, Bastani A. Documenting patient nutritional information in inpatient care. In proceeding of 7th Asia Pacific Conference on Clinical Nutrition (APCCN). Bangkok, Thailand: TJCN; 2011 5-9 June 2011 Contract No.: Document Number|.
  10. Schindler K, Pernicka E, Laviano A, Howard P, Schutz T, Bauer P, et al. How nutritional risk is assessed and managed in European hospitals: a survey of 21,007 patients findings from the 2007-2008 cross-sectional nutritionDay survey. Clin Nutr. [Multicenter Study Research Support, Non-U.S. Gov’t]. 2010;29(5):552-9.
  11. Anooj P. Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. JKSU-CIS. 2012;24(1):27-40.
  12. Girometti R, Fabris F, Sgarro A, Zanella G, Pullini S, Cereser L, et al. Differentiating Small (≤ 1 cm) Focal Liver Lesions as Metastases or Cysts by means of Computed Tomography: A Case-Study to Illustrate a Fuzzy Logic-Based Method to Assess the Impact of Diagnostic Confidence on Radiological Diagnosis. Computational and mathematical methods in medicine. 2014;2014.
  13. Hsu CY, Huang LC, Chen TM, Chen LF, Chao JC. A web-based decision support system for dietary analysis and recommendations. Telemedicine journal and e-health : the official journal of the American Telemedicine Association. 2011;17(2):68-75.
  14. Wirsam B, Hahn A, Uthus EO, Leitzmann C. Fuzzy sets and fuzzy decision making in nutrition. European journal of clinical nutrition. 1997;51(5):286-96.
  15. Asghari G, Ejtahed H, Sarsharzadeh M, Nazeri P, Mirmiran P. Designing Fuzzy Algorithms to Develop Healthy Dietary Pattern. Int J Endocrinol Metab. 2013;11(3):154-61.
  16. Zadeh LA. Fuzzy sets. Information and control. 1965;8(3):338-53.
  17. Zadeh LA. Is there a need for fuzzy logic? Information sciences. 2008;178(13):2751-79.
  18. Jensen GL, Friedmann JM, Coleman CD, Smiciklas-Wright H. Screening for hospitalization and nutritional risks among community-dwelling older persons. The American journal of clinical nutrition. 2001;74(2):201-5.
  19. Kondrup J, Rasmussen HH, Hamberg O, Stanga Z, Ad Hoc EWG. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. [Validation Studies]. 2003;22(3):321-36.
  20. Shikh F. [Manual of Diet Therapy]. 2nd ed. Tehran: Iranian Nutrition Society, Marz e danesh; 2006.
  21. Guney K, Sarikaya N. Comparison of Mamdani and Sugeno fuzzy inference system models for resonant frequency calculation of rectangular microstrip antennas. Progress In Electromagnetics Research B. 2009;12:81-104.
  22. Kong G, Xu D-L, Body R, Yang J-B, Mackway-Jones K, Carley S. A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. European Journal of Operational Research. 2012;219(3):564-73.
Health Management & Information Science
Volume 2, Issue 2 - Serial Number 2
10
April 2015
Pages 34-40

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