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

doi:10.18502/ijph.v52i1.11680

Mitra Montazeri Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Mehdi Ahmadinejad Department of Anesthesiology, Shahid Bahonar Hospital, Afzalipour Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran
Mahdieh Montazeri Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Kambiz Bahaadinbeigy Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Mohadeseh Montazeri Department of Computer, Technical and Vocational University, Kerman, Iran
Leila Ahmadian Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

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

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.