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Unplanned readmissions increase unnecessary health care costs and reduce the quality of care. It is essential to plan the discharge care from the beginning of hospitalization to reduce the risk of readmission. Machine learning-based readmission prediction models can support patients' preemptive discharge care services with improved predictive power. This study aimed to develop a readmission early prediction model utilizing nursing data for high-risk discharge patients. This retrospective study included the electronic medical records of 12,977 patients with 1 of the top 6 high-risk readmission diseases at a tertiary hospital in Seoul from January 2018 to January 2020. We used demographic, clinical, and nursing data to construct a prediction model. We constructed unplanned readmission prediction models by dividing them into Model 1 and Model 2. Model 1 used early hospitalization data (up to 1 day after admission), and Model 2 used all the data. To improve the performance of the machine learning method, we performed 5-fold cross-validation and utilized adaptive synthetic sampling to address data imbalance. The 6 algorithms of logistic regression, random forest, decision tree, XGBoost, CatBoost, and multiperceptron layer were employed to develop predictive models. The analysis was conducted using Python Language Reference, version 3.11.3. (Python Software Foundation). In Model 1, among the 6 prediction model algorithms, the random forest model had the best result, with an area under the receiver operating characteristic (AUROC) curve of 0.62. In Model 2, the CatBoost model had the best result, with an AUROC of 0.64. BMI, systolic blood pressure, and age consistently emerged as the most significant predictors of readmission risk across Models 1 and 2. Model 1, which enabled early readmission prediction, showed a higher proportion of nursing data variables among its important predictors compared to Model 2. Machine learning-based readmission prediction models utilizing nursing data provide basic data for evidence-based clinical decision support for high-risk discharge patients with complex conditions and facilitate early intervention. By integrating nursing data containing diverse patient information, these models can provide more comprehensive risk assessment and improve patient outcomes.

Intracranial aneurysm (IA) is difficult to detect, and most patients remain undiagnosed, as screening tests have potential risks and high costs. Thus, it is important to develop risk assessment system for efficient and safe screening strategy. Through previously published research, we have developed a prediction model for the incidence risk of IA using cohort observational data. This study was designed to verify whether such a prediction model also demonstrates sufficient clinical performance in predicting the prevalence risk at the point of health screening, using cross-sectional data. The study population comprised individuals who visited the Chonnam National University Hwasun Hospital Health Promotion Center in Korea for voluntary medical checkups between 2007 and 2019. All participants had no history of cerebrovascular disease and underwent brain CTA for screening purpose. Presence of IA was evaluated by two specialized radiologists. The risk score was calculated using the previously developed AI model, and 0 point represents the lowest risk and 100 point represents the highest risk. To compare the prevalence according to the risk, age-sex standardization using national database was performed. A study collected data from 5942 health examinations, including brain CTA data, with participants ranging from 20 to 87 years old and a mean age of 52 years. The age-sex standardized prevalence of IA was 3.20%. The prevalence in each risk group was 0.18% (lowest risk, 0–19), 2.12% (lower risk, 20–39), 2.37% (mid-risk, 40–59), 4.00% (higher risk, 60–79), and 6.44% (highest risk, 80–100). The odds ratio between the lowest and highest risk groups was 38.50. The adjusted proportions of IA patients in the higher and highest risk groups were 26.7% and 44.5%, respectively. The median risk scores among IA patients and normal participants were 74 and 54, respectively. The optimal cut-off risk score was 60.5 with an area under the curve of 0.70. We have confirmed that the incidence risk prediction model built through machine learning also shows viable clinical performance in predicting prevalence risk. By utilizing this prediction system, we can effectively predict not only the incidence risk but also the prevalence risk, which is the probability of already having the disease, using health screening data. This may enable us to consider strategies for the early detection of intracranial aneurysms.

Vibration and noise reduction are very important in electric vehicle driving motors. In this study, topology optimization of housing was performed to reduce vibration in a specific frequency caused by electromagnetic force generated by a permanent magnet synchronous motor (PMSM). The vibration induced by the electromagnetic force of the motor was calculated using electromagnetic-structural coupled analysis. Then, the magnitude of the acceleration for a specific frequency at which peak occurs in the rectangular and circular shape housing concept design model was reduced by using the topology optimization method. As a result, the rectangular and circular shape housing design reduced 92.9% and 96.0%, respectively. Finally, the vibration was effectively reduced while maintaining the electromagnetic characteristics of the motor, for which topology optimization was conducted while not changing the rotor or stator shape design (electromagnetic design factor) but by changing the motor housing shape design (mechanical and structural design factor).

Abstract In this study, we develop flexible multibody dynamic-electromagnetic-structural vibration coupled analysis method to accurately predict motor vibration by considering the electromagnetic force characteristics, rotating characteristics of rotating motor motors, and their interactions at the no-load rated speed and operating speed range. The structural characteristics are accurately reflected by developing a three-dimensional (3D) finite element model considering the entire components of the motor. The reliability of the 3D finite element model of the motor is verified using the impact hammer test. In addition, to consider the rotational characteristics of the rotor structure, we develop a flexible multibody dynamics model that connects the flexible rotor and the bearing with revolute joint. The vibration of the motor at the no-load rated speed is analyzed using flexible multibody dynamics-electromagnetic-structural vibration coupled analysis. Comparing the vibration test results, it is confirmed that the flexible multibody dynamics-electromagnetic-structural vibration coupled analysis result predicts the actual motor vibration more accurately than the conventional finite element analysis-based electromagnetic-structural vibration coupled analysis result. By using flexible multibody dynamics-electromagnetic-structural vibration coupled analysis in the operating speed range, it is confirmed that not only electromagnetic force harmonics but also sideband harmonics caused by rotor eccentricity-induced large vibrations, and also confirmed that it accurately predicts the vibration characteristics of actual motors with rotating rotors. Graphical Abstract Graphical Abstract

This study aimed to investigate whether occupational noise exposure is a risk factor for insomnia among male night-shift production workers. This study followed 623 male night-shift production workers at a tire manufacturing factory without insomnia for 4 years. Insomnia was evaluated based on the insomnia severity index at baseline and at 4-year follow-up. A score of ≥15 was defined as insomnia. The higher occupational noise exposure group was defined as those individuals exposed to 8-hour time-weighted-average noise above 80 dB (A). Participants' mean age was 46.3 ± 5.6 years. Of the 623 participants, 362 (58.1%) were in the higher occupational noise exposure group. At 4-year follow-up, insomnia occurred in 3.2% (n = 20) of the participants. In a multiple logistic regression analysis, the odds ratio of insomnia was 3.36 (95% confidence interval 1.083-10.405, P = 0.036) in the higher occupational noise exposure group when compared with the lower noise exposure group after adjusting for confounders. Our findings suggested that occupational noise exposure affected insomnia in male night-shift production workers. To prevent insomnia, efforts are required to reduce workplace noise exposure levels. Alternatively, moving to a less noisy work environment should be considered for workers with severe insomnia.

In this study, an electromagnetic force induced structural vibration for a brushless direct current (BLDC) motor with variable speed is performed using finite element analysis, as well as an experiment for operating speed range. The 3-D entire finite element model was used to predict the vibration characteristics for the operating speed range of a BLDC motor. To validate the finite element (FE) model, the modal analysis was compared with the results of a modal test. Then, for variable speed, electromagnetic force induced vibration characteristics for the range of operating speeds are predicted using the electromagnetic-structural vibration coupled analysis method. The predicted vibration characteristics are compared and validated with vibration experiment under the same operating conditions. Finally, it was found that the vibration characteristics predicted using the 3-D entire FE model can accurately reflect the actual vibration characteristics for variable speed.

In a MacPherson strut suspension, the side load is inevitably generated and it causes friction at the damper reducing riding comfort. In this paper, to solve this problem, progressive meta-model based sequential approximate optimization (SAO) is performed to minimize the side load. To calculate the side load, a wheel travel analysis is performed by using flexible multi-body dynamics (FMBD) model of suspension, which can consider both finite element method (FEM) and multi-body dynamics (MBD). In the optimal design process, meta-model is generated by using extracted sampling points and radial basis function (RBF) method. As a result of optimal design, spring setting positions that minimize the side load are obtained and by using optimal spring setting positions, the suspension FMBD model was constructed.

In this study, optimum design process to reduce a torque ripple was presented for an interior permanent magnet (IPM) type electric traction motor. Firstly, 2-D electromagnetic finite element analysis was conducted to analyze the characteristics of the torque ripple in a view of magnetic saturation as well as cogging torque. Then, design factors of stator shape and rotor shape affecting to the torque ripple was selected as design variables. Secondly, using selected design variables, sensitivity analysis was performed to select design variable more affecting to the torque ripple and output torque by using design of experiments method. Thirdly, radial based function meta-model was constructed based on the results of the design of experiments (DOE). Finally, the optimum design was performed by using the constructed meta-model and method of feasible direction. By using the presented optimum design process, it is confirmed that the torque ripple of the optimum design was reduced to compare with the initial design and that output torque performance of the optimum design was maintained.

Background In Korea, there were repeated radiation exposure accidents among non-destructive testing workers. Most of the cases involved local injury, such as radiation burns or hematopoietic cancer. Herein, we report a case of acute radiation syndrome caused by short periods of high exposure to ionizing radiation. Case presentation In January 2017, Korea Information System on Occupational Exposure (KISOE) found that a 31-year-old man who had worked in a non-destructive testing company had been overexposed to radiation. The patient complained of symptoms of anorexia, general weakness, prostration, and mild dizziness for several days. He was anemic. The venous injection areas had bruises and bleeding tendency. Blood and bone marrow testing showed pancytopenia and the patient was diagnosed with acute radiation syndrome (white blood cells: 1400/cubic mm, hemoglobin: 7.1 g/dL, platelets: 14000/cubic mm). He was immediately prohibited from working and blood transfusion was commenced. The patient’s radiation exposure dose was over 1.4 Gy (95% confidence limits: 1.1–1.6) in lymphocyte depletion kinetics. It was revealed that the patient had been performing non-destructive tests without radiation shielding when working in high places of the large pipe surface. Conclusions Exposure prevention is clearly possible in radiation-exposed workers. Strict legal amendments to safety procedures are essential to prevent repeated radiation exposure accidents.

Lead exposure is a risk factor for increased blood pressure and cardiovascular disease, even when blood lead levels (BLLs) are within the normal range. This study aimed to investigate the association between BLL and coronary artery stenosis (CAS) in asymptomatic adults using 128-slice dual-source coronary computed tomography (CT) angiography. We analyzed medical records data from 2,193 adults (1,461 men and 732 women) who elected to complete a screening health examination, coronary CT angiography, and BLL measurement during 2011-2018 and had no history of CAS symptoms, cardiovascular disease, or occupational exposure to lead. Logistic regression models were used to estimate associations between moderate-to-severe CAS ( stenosis) and a increase in blood lead, with and without adjustment for age, sex, hypertension, diabetes mellitus, dyslipidemia, body mass index, regular exercise, smoking status, and alcohol drinking. BLLs ranged from , with an arithmetic mean of . The arithmetic mean was higher for men than for women ( vs. , ) and higher in the moderate-to-severe CAS group than in the no-CAS or stenosis group ( vs. , ). Moderate-to-severe CAS was significantly associated with BLL before and after adjustment, with an adjusted odds ratio for a increase in BLL of 1.14 (95% CI: 1.02, 1.26), . BLL was positively associated with the prevalence of moderate-to-severe CAS in Korean adults who completed an elective screening examination for early cardiovascular disease, 94% of whom had a BLL of . More efforts and a strict health policy are needed to further reduce BLLs in the general population. https://doi.org/10.1289/EHP7351.