• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.533-539
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• 2023

This study aimed to analyze patients hospitalized for injuries who fell using the data from 2020 to 2021 at institution A located in Gangneung-si, Gangwon-do, using codes for causes of injury. After classifying 20 codes from W00 to W19, which are external cause codes for fall patients, the most frequently occurring W18, W01, W10, and W13 were analyzed. The external cause of injury code W18 was other falls on the same plane, with the highest frequency of S72 and Z47, S72 being a fracture of the femur, and Z47 being orthopedic follow-up treatment. The external injury code W01 was determined to be a fall on the same plane due to slipping, tripping, and tripping, and like W18, S72, a fracture of the femur, and Z47, orthopedic follow-up treatment, were frequently reported. In W10, intracranial injuries such as concussion and epidural hemorrhage due to a fall on the stairs, S06, were common. Lastly, in W13, 91% of cases occurred in people in their 40s to 70s due to falls from buildings or structures, confirming that they occur frequently in middle-aged people, Z47 had the most frequent orthopedic follow-up treatment, and S72 had a fracture of the femur. It was found to be the second most common. In this way, the frequency of falling patients was analyzed, and the age and main diagnosis code at which most falls occurred were analyzed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.526-532
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• 2023

Falls are fatal accidents that occur more than 420,000 times a year worldwide. Therefore, to study patients with falls, we found the association between extrinsic injury codes and principal diagnosis S-codes of patients with falls, and developed a prediction model to predict extrinsic injury codes based on the data of principal diagnosis S-codes of patients with falls. In this study, we received two years of data from 2020 and 2021 from Institution A, located in Gangneung City, Gangwon Special Self-Governing Province, and extracted only the data from W00 to W19 of the extrinsic injury codes related to falls, and developed a prediction model using W01, W10, W13, and W18 of the extrinsic injury codes of falls, which had enough principal diagnosis S-codes to develop a prediction model. 80% of the data were categorized as training data and 20% as testing data. The model was developed using MLP (Multi-Layer Perceptron) with 6 variables (gender, age, principal diagnosis S-code, surgery, hospitalization, and alcohol consumption) in the input layer, 2 hidden layers with 64 nodes, and an output layer with 4 nodes for W01, W10, W13, and W18 exogenous damage codes using the softmax activation function. As a result of the training, the first training had an accuracy of 31.2%, but the 30th training had an accuracy of 87.5%, which confirmed the association between the fall extrinsic code and the main diagnosis S code of the fall patient.