A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit
-
Mitra Montazeri
,
Mehdi Ahmadinejad
,
Mahdieh Montazeri
,
Kambiz Bahaadinbeigy
,
Mohadeseh Montazeri
,
Leila Ahmadian
Abstract
Background: Intensive Care Unit (ICU) has the highest mortality rate in the world. ICU has special equipment that leads to the hospital's most costly parts. The length of stay in the ICU is a special issue, and reducing this time is a practical approach. We aimed to use artificial intelligence to help early and timely diagnosis of the disease to help with health.
Methods: We designed a rule-based intelligent system to predict the length of stay and the mortality rate of trauma patients in ICU. A neuro-Fuzzy and eight machine learning models were used to predict the mortality rate in trauma patients in ICU. The performances of these techniques were evaluated with accuracy, sensitivity, specificity, and area under the ROC curve. Decision-Table was used to predict the length of stay in trauma patients in ICU. For comparison, eight machine learning models were used. The method is compared based on Mean absolute error and relative absolute error (%).
Results: Neuro-Fuzzy expert system and Decision-Table showed better results than other techniques. Accuracy, sensitivity, specificity, and ROC Area of Nero-Fuzzy are 83.6735, 0.9744, 0.3000, 0.8379, and 1, respectively. The mean absolute error and Relative absolute error (%) of the Decision-Table model are 4.5426 and 65.4391, respectively.
Conclusion: Neuro-Fuzzy expert system with the highest level of accuracy and a Decision-Table with the lowest Mean absolute error, which are rule-based models, are the best models. Therefore, these models are recommended as a valuable tool for prediction parameters of ICU as well as medical decision-making.
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33. Priyali S. Decision Tables of a Programmer: Meaning, Types and Advantages: yourarticlelibrary. Available from: www.yourarticlelibrary.com
2. Mukhopadhyay A, Tai BC, See KC, et al (2014). Risk factors for hospital and long-term mortality of critically ill elderly patients admitted to an intensive care unit. BioMed Res Int, 47: 777-780.
3. Rotta BP, Silva JMd, Fu C, Goulardins JB, Pires-Neto RdC, Tanaka C (2018). Relationship between availability of physiotherapy services and ICU costs. J Bras Pneumol, 960575.
4. Rapoport J, Teres D, Lemeshow S, Gehlbach S (1994). A method for assessing the clinical performance and cost-effectiveness of intensive care units: a multicenter inception cohort study. Crit Care Med, 22(9):1385-91.
5. Marini JJ, Wheeler AP (2010). Critical Care Medicine: The Essentials: Wollters Kluwer/Lippincott Williams & Wilkins.
6. McLeod R, Schell GP (2007). Management information systems. Pearson/Prentice Hall USA.
7. Nguyen TP, Ho TB (2012). Detecting disease genes based on semi-supervised learning and protein–protein interaction networks. Artif Intell Med , 54(1):63-71.
8. Latacz E, van Dam PJ, Vanhove C, et al (2021). Can medical imaging identify the histopathological growth patterns of liver metastases? Semin Cancer Biol, 71: 33-41.
9. Brown C, Chauhan J, Grammenos A, et al (2020). Exploring automatic diagnosis of covid-19 from crowdsourced respiratory sound data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
10. Baghel N, Dutta MK, Burget R (2020). Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network. Comput Methods and Programs Biomed, 197:105750.
11. Salah IB, De la Rosa R, Ouni K, Salah RB (2020). Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing. Biomed Signal Process Control, 57:101758.
12. Maldonado S, López J, Jimenez-Molina A, Lira H (2020). Simultaneous feature selection and heterogeneity control for SVM classification: An application to mental workload assessment. Expert Syst Appl, 143:112988.
13. Luo M, Zhao R (2018). A distance measure between intuitionistic fuzzy sets and its application in medical diagnosis. Artif Intell Med, 89:34-39.
14. Borra S, Di Ciaccio A (2010). Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Comput Stat Data Anal, 54(12):2976-89.
15. Molla MU, Giri BC, Biswas P (2021). Extended PROMETHEE method with Pythagorean fuzzy sets for medical diagnosis problems. Soft Comput, 25(6), 4503-4512.
16. McLachlan, Geoffrey J, Kim-Anh Do, Christophe Ambroise (2005). Ambroise Analyzing microarray gene expression data. Wiley Ser Probab Stat.
17. Cheng T-H, Hu PJ-H (2009). A data-driven approach to manage the length of stay for appendectomy patients. IEEE Trans Syst Man Cybern B Cybern, 39(6):1339-47.
18. Awad A, Bader–El–Den M, McNicholas J (2017). Patient length of stay and mortality prediction: A survey. Health Serv Manage Res, 30(2):105-20.
19. Garg L, McCLEAN S, Meenan BJ, Millard P (2011). Phase-type survival trees and mixed distribution survival trees for clustering patients' hospital length of stay. Informatica, 22(1):57-72.
20. Guzman Castillo M. Modelling patient length of stay in public hospitals in Mexico [PhD thesis]. University of Southampton, Malaysia ; 2012.
21. Freitas A, Silva-Costa T, Lopes F, et al (2012). Factors influencing hospital high length of stay outliers. BMC Health Serv Res, 12(1):265.
22. Le Gall JR, Lemeshow S, Saulnier F (1993). A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA, 270: 2957-2963.
23. Azari A, Janeja VP, Mohseni A (2012). Predicting hospital length of stay (PHLOS): A multi-tiered data mining approach. Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on; IEEE.
24. Dou L, Li X, Ding H, Xu L, Xiang H (2020). iRNA-m5C_NB: a novel predictor to identify RNA 5-Methylcytosine sites based on the Naive Bayes classifier. IEEE Access, 8:84906-17.
25. Xu F, Pan Z, Xia R (2020). E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework. Inf Process Manag, 57(5):102221.
26. Dumitru D (2009). Prediction of recurrent events in breast cancer using the Naive Bayesian classification. Annals of the University of Craiova-Mathematics and Computer Science Series, 36(2):92-6.
27. Breiman L (2001). Random forests. Machine Learning, 45(1):5-32.
28. Ferenc G, Lutovac M, Kvrgic V, Stepanic P (2013). A proposed approach to the classification of bearing condition using wavelets and random forests. Embedded Computing (MECO), 2013 2nd Mediterranean Conference on, IEEE.
29. Fathi M, Nemati M, Mohammadi SM, Abbasi-Kesbi R (2020). A machine learning approach based on SVM for classification of liver diseases. Biomed Eng, 32(03):2050018.
30. Yasoda K, Ponmagal R, Bhuvaneshwari K, Venkatachalam K (2020). Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA). Soft Computing, 24(21):16011-9.
31. Sha’abani M, Fuad N, Jamal N, Ismail M (2020). kNN and SVM classification for EEG: a review. InECCE, 2019, 555-65.
32. Tabrizi SS, Pashazadeh S, Javani V (2020). Comparative study of table tennis forehand strokes classification using deep learning and SVM. IEEE Sens J, 20(22):13552-61.
33. Priyali S. Decision Tables of a Programmer: Meaning, Types and Advantages: yourarticlelibrary. Available from: www.yourarticlelibrary.com
| Files | ||
| Issue | Vol 52 No 1 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v52i1.11680 | |
| Keywords | ||
| Rule based intelligent software Neuro-Fuzzy expert system Decision-table model Length of stay Mortality rate | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Montazeri M, Ahmadinejad M, Montazeri M, Bahaadinbeigy K, Montazeri M, Ahmadian L. A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit. Iran J Public Health. 2023;52(1):175-183.
