Use of neural network based on international classification ICD-10 in patients with head and neck injuries in Lublin Province, Poland, between 2006–2018, as a predictive value of the outcomes of injury sustained

Abstract

Introduction and objective. Head and neck injuries are a heterogeneous group in terms of both clinical course and prognosis. For years, there have been attempts to create an ideal tool to predict the outcomes and severity of injuries. The aim of this study was evaluation of the use of selected artificial intelligence methods for outcome predictions of head and neck injuries. Material sand Method. 6,824 consecutive cases of patients who sustained head and neck injuries, treated in hospitals in the Lublin Province between 2006-2018, whose data was provided by National Institute of Public Health / National Institute of Hygiene, were analyzed retrospectively. Patients were qualified using International Statistical Classification of Diseases and Related Health Problems (10th Revision). The multilayer perceptron (MLP) structure was utilized in numerical studies. Neural network training was achieved with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Results. In the designed network, the highest classification efficiency was obtained for the group of deaths (80.7%). The average value of correct classifications for all analyzed cases was 66%. The most important variable influencing the prognosis of an injured patient was diagnosis (weight 1.929). Gender and age were variables of less significance with weight 1.08 and 1.073, respectively. Conclusions. Designing a neural network was hindered due to the large amount of cases and linking of a large number of deaths with specific diagnosis (S06). With a predictive value of 80.7% for mortality, ANN can be a promising tool in the future; however, additional variables should be introduced into the algorithm to increase the predictive value of the network. Further studies, including other types of injuries and additional variables, are needed to introduce this method into clinical use.

Descriptive Analysis of Trauma Admission Trends before and during the COVID-19 Pandemic

“…The efficacy of neural networks in medical applications was first demonstrated by Penny and Frost in 1996 [ 26 ], showing potential comparable to clinical reasoning. Recent efforts to apply AI to injury outcome assessment using the International Classification of Diseases highlight the need for nonlinear predictive models [ 27 , 28 , 29 , 30 ].…”

Evaluating Changes in Trauma Epidemiology during the COVID-19 Lockdown: Insights and Implications for Public Health and Disaster Preparedness

“…The use of numerical, statistical, and machine-learning methods can complement typical experimental studies and may allow a better understanding of the complex relationships commonly found in medicine [20][21][22][23]. Statistical analyses based on the ICD-10 classification are particularly useful in trauma research because they allow the classification and comparison of different types of injuries and their prevalence in the population [24].…”