RESEARCH PAPER
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
1
Chair and Department of Trauma Surgery and Emergency Medicine, Medical University, Lublin, Poland
2
Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, University of Technology, Lublin,
Poland
3
I Department of Psychiatry, Psychotherapy and Early Intervention, Medical University, Lublin, Poland
4
Orthopaedic Department, Łęczna Hospital, Poland
5
Department of Computer Science, Faculty of Electrical Engineering and Computer Science, University of Technology, Lublin, Poland
Corresponding author
Mariusz Jojczuk
Chair and Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081, Lublin, Poland
Chair and Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081, Lublin, Poland
Ann Agric Environ Med. 2023;30(2):281-286
KEYWORDS
headTBIinjuryneckmortalityArtificial Neural Networkartificial intelligencemortality predictiontrauma scoring systeminjury scoring system
TOPICS
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 and methods:
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.
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 and methods:
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.
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