Explainable prediction of problematic smartphone use among South Korea's children and adolescents using a Machine learning approach.

BACKGROUND: Korea is known for its technological prowess, has the highest smartphone ownership rate in the world at 95%, and the smallest gap in smartphone ownership between generations. Since the onset of the COVID-19 pandemic, problematic smartphone use is becoming more prevalent among Korean children and adolescent owing to limited school attendance and outdoor activities, resulting in increased reliance on smartphones. 40.1% of adolescents are classified as high-risk, with only the adolescent group showing a persistent rise year after year. OBJECTIVE: The study purpose is to present data-driven analysis results for predicting and preventing smartphone addiction in Korea, where problematic smartphone use is severe. PARTICIPANTS AND METHODS: To predict the risk of problematic smartphone use in Korean children and adolescents at an early stage, we used data collected from the Smartphone Overdependence Survey conducted by the National Information Society Agency between 2017 and 2021. Eight representative machine and deep learning algorithms were used to predict groups at high risk for smartphone addiction: Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), extreme Gradient Boosting (XGBoost), Light GBM, Categorical Boosting, Multilayer Perceptron, and Convolutional Neural Network. RESULTS: The XGBoost ensemble algorithm predicted 87.60% of participants at risk of future problematic smartphone usebased on precision. Our results showed that prolonged use of games, webtoons/web novels, and e-books, which have not been found in previous studies, further increased the risk of problematic smartphone use. CONCLUSIONS: Artificial intelligence algorithms have potential predictive and explanatory capabilities for identifying early signs of problematic smartphone use in adolescents and young children. We recommend that a variety of healthy, beneficial, and face-to-face activities be offered as alternatives to smartphones for leisure and play culture.

Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death.

BACKGROUND: Korea has the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries. Consequently, central and local governments and private organizations in Korea cooperate in promoting various suicide prevention projects to actively respond to suicide problems. Machine learning has been used to predict suicidal ideation in the fields of health and medicine but not from a social science perspective. OBJECTIVE: Since suicidal ideation is a major predictor of suicide attempts, being able to anticipate and mitigate it helps prevent suicide. Therefore, this study presents a data-based analysis method for predicting suicidal thoughts quickly and effectively and suggests countermeasures against the causes of suicidal thoughts. PARTICIPANTS AND METHODS: To predict early signs of suicidal ideation in children and adolescents, big data collected for approximately 4 years (from 2017 to 2020) from the Korea Youth Policy Institute (NYPI) were used. To accurately predict suicidal ideation, supervised ma- chine learning classification algorithms such as logistic regression, random forest, XGBoost, multilayer perceptron (MLP), and convolutional neural network (CNN) were used. RESULTS: Using CNN, suicidal ideation was predicted with an accuracy of approximately 90 %. The logistic regression results showed that sadness and depression increased suicidal thoughts by more than 25 times, and anxiety, loneliness, and experience of abusive language increased suicidal thoughts by more than three times. CONCLUSIONS: Machine learning and deep learning approaches have the potential to predict and respond to suicidal thoughts in children, adolescents, and the general population, as well as help respond to the suicide crisis by preemptively identifying the cause.

A Case Study on Community Treatment Center for Response to COVID-19 in Korea: Focusing on Surge Capacity 4S Component Analysis and Social Well-Being Perspective.

In March, 2020, during the COVID-19 pandemic in Korea, the first Community Treatment Center (CTC), which is a motel-type Alternate Care Site (ACS) for mild and asymptomatic patients, was opened. This is a case study of the first Community treatment center prepared to respond to COVID-19. One of the researchers worked as a medical doctor in one of the CTCs operated by the Korean government. The CTC's eight medical staff members were interviewed in-depth one-on-one. Then the data obtained from observation, collection, and interview were triangulated. In this study, it was identified based on the 4S factor that evaluates the surge capacity to meet the medical needs of CTC. And how the CTC was operated from a medical and social welfare perspective and what problems appeared to patients during the operation were analyzed. Three dormitories of a national training center were used as the CTC. Each patient used a room equipped with a toilet, a shower, and a washbasin. Medical staff and government officials with various backgrounds were dispatched. Telemedicine was also used to prevent the spread of infection. The CTC made a significant contribution to both medical and social welfare fields. It provided patients psychological stability in a comfortable environment. But some patients had psychological problems and difficulties involving work and family care. Various efforts in conjunction with participation from social workers are required to reduce these problems.

Small cell carcinoma presenting as a biatrial mass with obstructive physiology: a case report.

BACKGROUND: Small cell carcinoma is a highly aggressive and often fatal cancer that most commonly arises in the lung, although it can occasionally arise from other sites, such as the gastrointestinal tract, prostate or cervix. Cardiac involvement, however, is extremely uncommon and therefore has been poorly documented in the literature. CASE PRESENTATION: We describe a rare case of a 31-year-old male with small cell carcinoma presenting as a massive, 15-cm cardiac tumor invading the bilateral atria, interatrial septum, and pericardium without an apparent primary malignancy on PET CT and cardiac MRI. With extensive tissue necrosis, traditional methods of obtaining a right atrial endomyocardial biopsy via internal jugular venous access failed and a diagnosis was made via endoscopic ultrasound guided transesophageal fine needle aspiration of the left atrial mass. Due to the extensive tumor invasion, the patient was not a suitable candidate for surgical resection, debulking, or heart transplant. The patient was treated with etoposide, carboplatin, atezolizumab, and radiation therapy with initial monitoring in the intensive care unit due to concern that tumor lysis may cause rapid cardiac decompensation. Unfortunately, 4 months after chemoradiation therapy, the malignancy progressed and the patient passed away 6 months after the initial diagnosis. CONCLUSION: We describe a rare occurrence of small cell carcinoma presenting as a massive cardiac tumor without apparent primary malignancy. This case demonstrates useful alternative diagnostic strategies and treatment considerations for patients presenting with a rare cardiac mass.

A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor.

Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment6-9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.

Apolipoprotein E mediation of neuro-inflammation in a murine model of multiple sclerosis.

Apolipoprotein E (ApoE) functions as a ligand in receptor-mediated endocytosis of lipoprotein particles and has been demonstrated to play a role in antigen presentation. To explore the contribution of ApoE during autoimmune central nervous system (CNS) demyelination, we examined the clinical, cellular immune function, and pathologic consequences of experimental autoimmune encephalomyelitis (EAE) induction in ApoE knockout (ApoE(-/-)) mice. We observed reduced clinical severity of EAE in ApoE(-/-) mice in comparison to WT mice that was concomitant with an early reduction of dendritic cells (DCs) followed by a reduction of additional innate cells in the spinal cord at the peak of disease without any differences in axonal damage. While T cell priming was enhanced in ApoE(-/-) mice, reduced severity of EAE was also observed in ApoE(-/-) recipients of encephalitogenic wild type T cells. Expression of ApoE during EAE was elevated within the CNS of wild type mice, particularly by innate cells such as DCs. Overall, ApoE promotes clinical EAE, likely by mediation of inflammation localized within the CNS.

Implementing housing first in rural areas: pathways Vermont.

The benefits of Pathways Housing First in addressing chronic homelessness for persons with severe mental illness have been well established. However, the implementation and effectiveness of such programs in rural areas has yet to be examined. We described the model's adaptations in Vermont, including the use of hybrid assertive community treatment-intensive case management teams, which consisted of service coordinators with geographically based caseloads (staff/client ratio of 1:20) and regional multidisciplinary specialists. The program's innovative and widespread inclusion of technology into operations facilitated efficiency and responsiveness, and a pilot telehealth initiative supplemented in-person client visits. The program achieved a housing retention rate of 85% over approximately 3 years, and consumers reported decreased time spent homeless, demonstrating that program adaptations and technological enhancements were successful.