• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.1-9
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• 2024

Bridges, which are construction structures, have increased from 584 to 38,405 since the 1970s. However, as the number of bridges increases, the number of bridges with a service life of more than 30 years increases to 21,737 (71%) by 2030, resulting in fatal accidents due to basic human resource maintenance of facilities. Accordingly, the importance of bridge safety inspection and maintenance measures is increasing, and the need for decision-making support for supervisors who manage multiple bridges is also required. Currently, the safety inspection and maintenance method of bridges is to write down damage, condition, location, and specifications on the exterior survey map by hand or to record them by taking pictures with a camera. However, errors in notation of damage or defects or mistakes by supervisors are possible, typos, etc. may reduce the reliability of the overall safety inspection and diagnosis. To improve this, this study visualizes damage data recorded in the BIM model in an AR environment and proposes a maintenance plan for bridges with a small number of people through maintenance decision-making support for supervisors.