Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

Background: Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machine learning techniques.

Allergy Wheal and Erythema Segmentation Using Attention U-Net.

The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera.

Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.

Purpose: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy.

Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy.

Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort.

Purpose: Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based decision support system for chest radiography (CXR). This retrospective study aimed to evaluate the concordance rate of radiologists and Lunit for thoracic abnormalities in a multicenter health screening cohort.

Methods And Materials: We retrospectively evaluated the radiology reports and Lunit results for CXR at several health screening centers in August 2020.

Changes in Nurse Staffing Grades of Korean Hospitals during COVID-19 Pandemic.

The global COVID-19 pandemic is creating challenges to manage staff ratios in clinical units. Nurse staffing level is an important indicator of the quality of care. This study aimed to identify any changes in the nurse staffing levels in the general wards of hospitals in Korea during the COVID-19 pandemic.

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques.

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, = 213; no OSA, = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, = 149; no OSA, = 46) and test data (OSA, = 64; no OSA, = 20).

Electrocatalytic Oxidation of Methanol by a Polymeric Ni Complex-Modified Electrode Prepared by a One-Step Cold-Plasma Process.

In this work, a polymeric nickel complex-modified indium tin oxide (ITO) electrode was prepared by a one-step cold-plasma process of acrylic-Ni complex precursors. Also, the work provides the electrocatalytic oxidation of methanol by a polymeric Ni complex-modified electrode prepared by a simple one-step cold-plasma process. The acrylic-Ni complex precursors were synthesized by complexation of nickel (II) chloride, and acrylic acid in a small amount of water; subsequently we added N,N'-methylene-bis-acrylamide as a crosslinking agent to the complex solution.