Development of neural network models for prediction of the outcome of COVID-19 hospitalized patients based on initial laboratory findings, demographics, and comorbidities

Mirza Pasic; Edin Begic; Faris Kadic; Ali Gavrankapetanovic; Mugdim Pasic

doi:10.4103/jfmpc.jfmpc_113_22

Development of neural network models for prediction of the outcome of COVID-19 hospitalized patients based on initial laboratory findings, demographics, and comorbidities

J Family Med Prim Care. 2022 Aug;11(8):4488-4495. doi: 10.4103/jfmpc.jfmpc_113_22. Epub 2022 Aug 30.

Authors

Mirza Pasic¹, Edin Begic^{2

3}, Faris Kadic⁴, Ali Gavrankapetanovic⁵, Mugdim Pasic¹

Affiliations

¹ Department of Industrial Engineering and Management, Mechanical Engineering Faculty, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina.
² Department of Cardiology, General Hospital "Prim. dr. Abdulah Nakaš", 71000 Sarajevo, Bosnia and Herzegovina Department of Pharmacology, Faculty of Medicine, University School of Science and Technology, 71000 Sarajevo, Bosnia and Herzegovina.
³ Department of Cardiology, General Hospital "Prim. dr. Abdulah Nakaš", 71000 Sarajevo, Bosnia and Herzegovina Gavrankapetanovic - Department of Surgery, General Hospital "Prim. dr. Abdulah Nakaš", 71000 Sarajevo, Bosnia and Herzegovina.
⁴ Department of Internal Medicine, General Hospital "Prim.Dr. Abdulah Nakaš", Sarajevo, Bosnia and Herzegovina.
⁵ Department of Surgery, General Hospital "Prim.Dr. Abdulah Nakaš", Sarajevo, Bosnia and Herzegovina.

Abstract

Background: During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals.

Methods: The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals.

Results: In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died.

Conclusion: Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate.

Keywords: Classification; covid-19; machine learning; neural networks; prediction.