A5019904818- Nutrition and Health in Aging
- Nutritional Studies and Diet
- Frailty in Older Adults
- Dermatological and COVID-19 studies
- Cleft Lip and Palate Research
- Stoma care and complications
- Cancer survivorship and care
- Child Nutrition and Water Access
Abstract Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening often overlooked or inaccurate due to lack of awareness experience among health care providers. This study aimed develop validate novel digital smartphone‐based self‐administered tool that uses facial features, especially the ocular area, as indicators inpatient Facial photographs scales were collected from 619 four different hospitals. A machine learning...
Background Malnutrition affects many worldwide, necessitating accurate and timely nutritional risk assessment. This study aims to develop validate a machine learning model using facial feature recognition for predicting risk. innovative approach seeks offer non-invasive, efficient method early identification intervention, ultimately improving health outcomes. Methods We gathered medical examination data images from 949 patients across multiple hospitals predict status. In this multicenter...
Malnutrition is a common and severe problem in patients with cancer that directly increases the incidence of complications significantly deteriorates quality life. Nutritional risk screening dietary assessment are critical because they basis for providing personalized nutritional support. No digital smartphone-based self-administered tool among hospitalized has been developed evaluated.This study aims to develop mini program evaluate validity program.We have R+ Dietitian program, which...
Objective Malnutrition is prevalent among cancer patients, smartphone-based self-administered nutritional assessment tools offer a promising solution for effective screening. This study aims to retrospectively analyze the relationships between status evaluated by digital tool (R+ Dietitian) and clinicopathologic factors of patients. Methods Cancer patients who met inclusion criteria were divided into two subgroups based on age, Nutritional Risk Screening-2002, Patient-Generated Subjective...