Prediction of Treatment Cost by Artificial Neural Network of Patients with COVID-19 in Intensive Care Unit

Abstract

Objective: Artificial neural networks (ANNs) are computer systems that are inspired by the biological neural networks that make up mammalian brains. An ANN is built from a network of linked units or nodes known as artificial neurons, which are roughly modeled after the neurons in the human brain. Each link, like synapses in a human brain, has the ability to send a signal to other neurons. The connections are referred to as edges. Neurons and edges usually have a weight that changes as learning progresses. The weight changes the intensity of the signal at a connection. Artificial neural networks have found applications in a wide range of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control, medical diagnostics, data mining, visualization, machine translation, distinguishing highly invasive cancer cell lines from less invasive lines using simply cell shape information, and many more domains are examples of application areas. In this study, ANN analysis was utilized by us to forecast the total cost of therapy or the prognosis of severe COVID-19 the patients in the intensive care unit (ICU). Methods: The parameters such as ages, and the other biochemical parameters that affect the staying periods (days) of COVID-19 infected patients in ICU were evaluated by using an ANN analysis. For this a computer program, Pythia®, was used to develop ANN models. Real data was used for that selected patients in this study. Results: The real data obtained from the ICU and gave to the computer as initial parameters. The computer program gave 15 neurons for the first level, one neurons for the second level as the most suitable model for the prediction (SSD = 0.000995). This program predicts a total cost 144.930,94 Turkish Lira (27.300 USD) where the real cost 142.234,06 Turkish Lira (26.792 USD) for the real patient in 2019. This relation was found to be good to predict the possible affected parameters on staying times. Conclusion: The ANN model developed and released in this research does not necessitate any experimental parameters. Besides, ANN has the ability to deliver helpful and exact prediction or information regarding the expense of COVID-19 patients in ICU. © 2022. All Rights Reserved.