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.