Methods: The National Societies for Emergency Medicine of Hong Kong, India, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand and Turkey participated in the joint Japanese Association of Acute Medicine (JAAM) and Asian Conference of Emergency Medicine (ACEM) Special Symposium held in October 2013 at Tokyo, Japan. The findings are reviewed in this paper.
Results: Emergency medicine (EM) has over the years evolved into a distinct and recognized medical discipline requiring a unique set of cognitive, administrative and technical skills for managing all types of patients with acute illness or injury. EM has contributed to healthcare by providing effective, safe, efficient and cost-effective patient care. Integrated systems have developed to allow continuity of emergency care from the community into emergency departments. Structured training curriculum for undergraduates, and specialty training programs for postgraduates are in place to equip trainees with the knowledge and skills required for the unique practice of EM.
Conclusion: The practice of EM still varies among the Asian countries. However, as a region, we strive to continue in our efforts to develop the specialty and improve the delivery of EM.
METHODS: LDA was applied to 6,328 Taiwanese clinical patients for classification purposes. Clustering method was used to identify the associated influential symptoms for each severity level.
RESULT: LDA shows only 36 HAICDDS questions are significant to distinguish the 5 severity levels with 80% overall accuracy and it increased to 85.83% when combining normal and MCI groups. Severe dementia patients have the most serious declination in most cognitive and functionality domains, follows by moderate dementia, mild dementia, MCI and normal patients.
CONCLUSION: HAICDDS is a reliable and time-saved diagnosis tool in classifying the severity of dementia before undergoing a more in-depth clinical examination. The modified CDR may be indicated for epidemiological study and provide a solid foundation to develop a machine-learning derived screening instrument to detect dementia symptoms.