The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can improve stroke prognosis. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Medical service use and health behavior data are easier to collect than medical imaging data. Here, we used a deep neural network to detect stroke using medical service use and health behavior data; we identified 15,099 patients with stroke. Principal component analysis (PCA) featuring quantile scaling was used to extract relevant background features from medical records; we used these to predict stroke. We compared our method (a scaled PCA/deep neural network [DNN] approach) to five other machine-learning methods. The area under the curve (AUC) value of our method was 83.48%; hence; it can be used by both patients and doctors to prescreen for possible stroke.

A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.

Underweight premenopausal women are at a higher risk of low bone mass and low skeletal muscle. Educational efforts that promote a normal weight in premenopausal women should be reinforced.

Background We hypothesized that portal vein tumor thrombosis (PVTT) in hepatocellular carcinoma (HCC) increases portal pressure and causes esophageal varices and variceal bleedings. We examined the incidence of high-risk varices and variceal bleeding and determined the indications for variceal screening and prophylaxis. Methods This study included 1709 asymptomatic patients without any prior history of variceal hemorrhage or endoscopic prophylaxis who underwent upper endoscopy within 30 days before or after initial anti-HCC treatment. Of these patients, 206 had PVTT, and after 1:2 individual matching, 161 of them were matched with 309 patients without PVTT. High-risk varices were defined as large/medium varices or small varices with red-color signs and variceal bleeding. Bleeding rates from the varices were compared between matched pairs. Risk factors for variceal bleeding in the entire set of patients with PVTT were also explored. Results In the matched-pair analysis, the proportion of high-risk varices at screening (23.0% vs. 13.3%; P = 0.003) and the cumulative rate of variceal bleeding (4.5% vs. 0.4% at 1 year; P = 0.009) were significantly greater in the PVTT group. Prolonged prothrombin time, lower platelet count, presence of extrahepatic metastasis, and Vp4 PVTT were independent risk factors related to high-risk varices in the total set of 206 patients with PVTT (Adjusted odds ratios [95% CIs], 1.662 [1.151–2.401]; 0.985 [0.978–0.993]; 4.240 [1.783–10.084]; and 3.345 [1.457–7.680], respectively; Ps < 0.05). During a median follow-up of 43.2 months, 10 patients with PVTT experienced variceal bleeding episodes, 9 of whom (90%) had high-risk varices. Presence of high-risk varices and sorafenib use for HCC treatment were significant predictors of variceal bleeding in the complete set of patients with PVTT (Adjusted hazard ratios [95% CIs], 26.432 [3.230–216.289]; and 5.676 [1.273–25.300], respectively; Ps < 0.05). Conclusions PVTT in HCC appears to increase the likelihood of high-risk varices and variceal bleeding. In HCC patients with PVTT, endoscopic prevention could be considered, at least in high-risk variceal carriers taking sorafenib.

Multi‐valued logic (MVL) computing, which uses more than three logical states, is a promising future technology for handling huge amounts of data in the forthcoming “big data” era. The feasibility of MVL computing depends on the development of new concept devices/circuits beyond the complementary metal oxide semiconductor (CMOS) technology. This is because many CMOS devices are required to implement basic MVL functions, such as multilevel NOT, AND, and OR. In this study, a novel MVL device is reported with a complementarily controllable potential well, featuring the negative differential transconductance (NDT) phenomenon. This NDT device implemented on the WS2–graphene–WSe2 van der Waals heterostructure is evolved to a double‐NDT device operating on the basis of two consecutive NDT phenomena via structural engineering and parallel device configuration. This double‐NDT device is intensively analyzed via atomic force microscopy, kelvin probe force microscopy, Raman spectroscopy, and temperature‐dependent electrical measurement to gain a detailed understanding of its operating mechanism. Finally, the operation of a quaternary inverter configured with the double‐peak NDT device and a p‐channel transistor through Cadence circuit simulation is theoretically demonstrated.

[Purpose] The purpose of this study was to understand factors present at baseline that affect outcome and healthcare utilization post-stroke. We investigated the association between the Charlson Comorbidity Index (CCI) score and functional outcome (length of stay) after hemorrhagic and ischemic stroke. [Subjects and Methods] Data from the Korean National Hospital Discharge In-depth Injury Survey for 6 years, from 2005 to 2010, were used. The t-test and analysis of variance were carried out to compare average differences in the length of stay with the general characteristics in accordance with CCI. Multiple regression analysis was carried out using dummy variables to look at factors affecting stroke patients’ length of stay. [Results] Independent variables with significant relationships with the log-transformed length of stay included gender, type of insurance, the size of city of residence, the number of beds in the hospital, the location of the medical institution, hospitalization path, receipt of physical therapy, treatment involving brain surgery, death, the type of stroke, and CCI. [Conclusion] The results of the present study suggests that CCI independently influences the length of stay after ischemic and hemorrhagic stroke and that variables with significant relationships with the log-transformed length of stay need to be continuously managed.

The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.

Background The incidence of hepatocellular carcinoma (HCC) has been increasing among the elderly populations. Trans-arterial chemoembolization (TACE), a widely used first-line non-curative therapy for HCCs is an issue in geriatrics. We investigated the prognosis of elderly HCC patients treated with TACE and determined the factors that affect the overall survival. Methods We included 266 patients who were older than 65 years and had received TACE as initial treatment for HCC. We analyzed the skeletal muscle index (SMI) and visceral-to-subcutaneous fat ratio (VSR) around the third lumbar vertebrae using computed tomography scans. Muscle depletion with visceral adiposity (MDVA) was defined by falling below the median SMI and above the median VSR value sex-specifically. We evaluated the overall survival in association with MDVA and other clinical factors. Results The mean age was 69.9 ± 4.5 years, and 70.3% of the patients were men. According to the Barcelona Clinic Liver Cancer (BCLC) staging system, 29, 136, and 101 patients were classified as BCLC 0, A, and B stages, respectively, and 79 (29.7%) had MDVA. During the median follow-up of 4.1 years, patients with MDVA had a shorter life expectancy than those without MDVA (P = 0.007) even though MDVA group had a higher objective response rate after the first TACE (82.3% vs. 75.9%, P = 0.035). Multivariate analysis revealed that MDVA (Hazard ratio [HR] 1.515) age (HR 1.057), liver function (HR 1.078), tumor size (HR 1.083), serum albumin level (HR 0.523), platelet count (HR 0.996), tumor stage (stage A, HR 1.711; stage B, HR 2.003), and treatment response after the first TACE treatment (HR 0.680) were associated with overall survival. Conclusions MDVA is a critical prognostic factor for predicting survival in the elderly patients with HCC who have undergone TACE.