ABSTRACT
PURPOSE: There are various neurogenic bladder patterns that occur in patients during stroke. Among these patterns, the focus was mainly on the patient's facial parsy diagnosis. Stroke requires early response, and it is most important to identify initial symptoms such as facial parsy. There is an urgent need for a diagnostic technology that notifies patients and caregivers of the onset of disease in the early stages of stroke. We developed an artificial intelligence (AI) stroke early-stage analysis software that can alert the early stage of stroke through analysis of facial muscle abnormalities for the elderly neurogenic bladder prevention. METHODS: The method proposed in this paper developed a learning-based deep learning analysis technology that outputs the initial stage of stroke after acquiring a high-definition digital image and then deep learning face analysis. The applied AI model was applied as a multimodal deep learning concept. The system is linked and integrated with the existing urine management integrated system to support patient management with a total-care concept. RESULTS: We developed an AI stroke early-stage analysis software that can alert the early stage of stroke with 86% hit performance through analysis of facial muscle abnormalities in the elderly. This result shows the validation result of the landmark image learning model based on the distance learning model. CONCLUSION: We developed an AI stroke early-stage diagnostic system as a wellness personal medical service plan and prevent cases of missing golden time when existing stroke occurs. In order to secure and facilitate distribution of this, it was developed in the form of AI analysis software so that it can be mounted on various hardware products. In the end, it was found that using AI for these stroke diagnoses and making them quickly and accurately had a positive effect indirectly, if not directly, on the neurogenic bladder.
ABSTRACT
AIM: There have been few studies done looking at the relationship between resilience, Type D personality, and self-care behavior in patients with heart failure. The purpose of this study was to identify the mediating effect of resilience on the relationship between Type D personality and self-care behavior in patients with heart failure. METHODS: The subjects consisted of a sample of 90 heart failure patients aged ≥20 years who visited the cardiology outpatient clinic in the Chungbuk area of South Korea. RESULTS: Among 90 patients, 49 subjects (54.0%) were classified as Type D personality, who exhibited statistically significant differences in resilience and self-care behavior (p < .001). A statistically significant correlation was also observed between self-care behavior score and resilience score (p < .01). The resilience had full mediation effects on the relationship between Type D personality and self-care behavior. In other words, the higher their resilience, the better their self-care behavior. CONCLUSIONS: The study showed that resilience and Type D personality have important effects on self-care behavior.
