SavedAdd your journal

Journals / Eurasian Journal of Medical Investigation / 2022 / Cilt: 6 - Sayı: 1

Determining the Length of Stay in the Intensive Care Unit of Patients With Sepsis Who Underwent Hemoadsorption Using the Artificial Neural Networks Model

Suna KOC
Harun UYSAL
Pages
70–77
Publication Date
2022-10-12
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: The artificial neural network modeling and this prediction were found to be useful for predicting patients staying time at ICU

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objectives: The artificial neural network (ANN) analysis was used to predict the exact staying period of patients with sepsis have undergone hemoadsorption hospitalized in the General Intensive Care Unit (ICU) of X University Hospital.
📊
Key Findings & Data:Methods: ANN model was successfully developed having 21 neurons in total (11 neurons at the first level, 9 neurons at the second level, and 1 neuron at the third level) using a computer program.
💡
Conclusion & Impact:Conclusion: The artificial neural network modeling and this prediction were found to be useful for predicting patients staying time at ICU
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Objectives: The artificial neural network (ANN) analysis was used to predict the exact staying period of patients with sepsis have undergone hemoadsorption hospitalized in the General Intensive Care Unit (ICU) of X University Hospital. Methods: ANN model was successfully developed having 21 neurons in total (11 neurons at the first level, 9 neurons at the second level, and 1 neuron at the third level) using a computer program. The created network was trained using our real data and the fermi function of the system was determined by the program and then the program was able to predict possible staying time prior to real-time. Main variables of interest: Staying periods (days), ages, number of comorbidities, hemoperfusion periods (days), blood pH, C-Reactive Protein, procalcitonin, and blood lactate levels were inputs. Results: Predicted values were plotted to real data and the determination coefficient was found to be r2= 0.802. This relation was found to be good to predict the possible staying times of patients with sepsis treated using hemoadsorption in the ICU for a better organization and reducing the total cost. Conclusion: The artificial neural network modeling and this prediction were found to be useful for predicting patients staying time at ICU

🔖

Cite This Article

KOC, S., UYSAL, H. (2022). Determining the Length of Stay in the Intensive Care Unit of Patients With Sepsis Who Underwent Hemoadsorption Using the Artificial Neural Networks Model. Eurasian Journal of Medical Investigation, Cilt: 6 - Sayı: 1, 70–77. https://ajindex.com/articles/508523

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Kato T, Fujino K, Tsujita Y, Miyatake H, Tabata T, Eguchi Y. Impact of abdominal aortic calcification on 90-day mortality in sepsis patients: a pilot retrospective cohort study. Acute Med Surg 2021;8:e630
  2. Honoré PM, De Bels D, Barreto Gutierrez L, Spapen HD. Hemoadsorption therapy in the critically ill: solid base but clinical haze. Ann Intensive Care 2019;9:22
  3. Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in glioblastoma patients. J Biomed Inform 2021;115:103694
  4. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016:315:801–10
  5. Xu X, Jia C, Luo S, Li Y, Xiao F, Dai H, et al. Effect of HA330 resindirected hemoadsorption on a porcine acute respiratory distress syndrome model. Ann Intensive Care 2017;7:84
  6. Değim T, Hadgraft J, Ilbasmis S, Ozkan Y. Prediction of skin penetration using artificial neural network (ANN) modeling. J Pharm Sci 2003;92:656–64
  7. Aslan C, Çelebi N, Değim IT, Atak A, Özer Ç. Development of interleukin-2 loaded chitosan-based nanogels using artificial neural networks and investigating the effects on wound heal ing in rats. AAPS Pharm Sci Tech 2017;18:1019–30
  8. Degim T. Understanding skin penetration: computer aided modeling and data interpretation. Current Computer-Aided Drug Design 2005;1:11–9
  9. Wan Z, Xu Y, Šavija B. On the use of machine learning models for prediction of compressive strength of concrete: influence of dimensionality reduction on the model performance. Materials (Basel) 2021;14:713
  10. Patro R, Norel R, Prill RJ, Saez-Rodriguez J, Lorenz P, Steinbeck F, et al. A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin. BMC Bioinformatics 2016;17:155
  11. László Galata D, Könyves Z, Nagy B, Novák M, Alexandra Mészáros L, Szabó E, et al. Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data. Int J Pharm 2021;597:120338
  12. Takayama K, Fujikawa M, Obata Y, Morishita M. Neural network based optimization of drug formulations. Adv Drug Deliv Rev 2003;55:1217–31
  13. Huang Z, Wang SR, Su W, Liu JY. Removal of humoral mediators and the effect on the survival of septic patien