Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach.

Background: Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries.

Objective: In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs).

Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment.

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity.

Encapsulation of GG Using Milk Protein-Based Delivery Systems: Effects of Reaction Temperature and Holding Time on Their Physicochemical and Functional Properties.

Microencapsulation is a protective process for materials that are sensitive to harsh conditions encounted during food manufacture and storage. The objectives of this research were to manufacture a milk protein-based delivery system (MPDS) containing GG (LGG) using skim milk powder and to investigate the effects of manufacturing variables, such as reaction temerpature and holding time, on the physiccohemical properties of MPDS and viability of LGG under dairy food processing and storage conditions. MPDS was prepared using chymosin at varing reaction temperatures from 25°C to 40°C for 10 min and holding times from 5 to 30 min at 25°C.