Development and Validation of Parental Self-Efficacy Scale for Dietary Management of Children with Food Allergies.

Background: It is necessary to evaluate the parental ability to achieve nutritional adequacy while avoiding allergens for children with food allergies (FA), yet this area is currently understudies. Our study aimed to develop and validate the Parental Self-Efficacy Scale for Dietary Management of Children with Food Allergies (PSED-FA).

Methods: PSED-FA was developed through a literature review and semi-structured interviews refined by a panel of five allergists and five dietitians, and validated using responses from caregivers of children with FA.

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary.

Challenges of diet planning for children using artificial intelligence.

Background/objectives: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications.