Mojtaba Johari Moghadam1*, Nazanin Johari Moghadam2 and Adel Joharimoghadam3
 
1 School of Mechanical Engineering, Iran University of Science & Technology, Tehran, Iran, Email: [email protected]
2 MA Bio Medical Engineering, Islamic Azad University, Science and Research branch, Tehran, Iran
3 Department of Cardiology, AJA University of Medical Sciences, Tehran, Iran
 
*Correspondence: Mojtaba Johari Moghadam, School of Mechanical Engineering, Iran University of Science & Technology, Tehran, Iran, Tel: 00989127110017, Email: [email protected]

Citation: Moghadam MJ, et al. Prediction of Mortality Rate among ICU Patients based on Vital Signals using Data Analysis Methods. Ann Med Health Sci Res. 2019;9:603- 607

This open-access article is distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC) (http://creativecommons.org/licenses/by-nc/4.0/), which permits reuse, distribution and reproduction of the article, provided that the original work is properly cited and the reuse is restricted to noncommercial purposes. For commercial reuse, contact [email protected]

Abstract

Using a scoring system for illness intensity can be a signpost for the physician to objectively assess the future state of the patient or estimate the probability of his/her recovery. In fact, due to individual differences among ICU patients, it seems necessary to develop a special estimation system for every intensive care unit. This system helps physicians to prioritize the patients in receiving appropriate care and helps them to be more explicit in explaining and presenting the final state of the illness to the patients’ affiliates. By analyzing the vital signals of ICU patients, their mortality rate can be automatically predicted. In our proposed system, using statistical analyses and Wavelet change, several features were extracted out of the patients’ vital signals. Then using Fisher’s standard analysis and multi-layered perceptron neural network classification, it was found that the proposed system is able to predict people’s mortality rate with high accuracy. The results of the present study indicate that the proposed system has an accuracy level of 85.1% and 81.7% for treatment and test data, respectively. Any attempt to practically apply this method can help enhance the quality of care system in ICU sections of the hospitals considerably.

+44 7460312890
Emerging Sources Citation Index (ESCI)
Emerging Sources Citation Index
Emerging Sources Citation Index
Indexed in

PubMed Central Index Copernicus Emerging Sources Citation Index
Abstracted/Indexed in

  • Include Baidu Scholar
  • CNKI (China National Knowledge Infrastructure)
  • EBSCO Publishing's Electronic Databases
  • Exlibris – Primo Central
  • Google Scholar
  • Hinari
  • Infotrieve
  • National Science Library
  • ProQuest
  • TdNet
  • African Index Medicus
Annals of Medical and Health Sciences Research The Annals of Medical and Health Sciences Research is a bi-monthly multidisciplinary medical journal. more >>
Submit your Manuscript

Recommended Conferences