4.Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology
Hye-Geum KIM ; Dai-Seg BAI ; Bon-Hoon KOO ; Eun-Jin CHEON ; Seokho YUN ; So Hye JO ; Byoungyoung GU
Journal of Korean Medical Science 2025;40(9):e20-
Background:
Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia.
Methods:
A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split.
Results:
The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts.
Conclusion
The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.
5.Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology
Hye-Geum KIM ; Dai-Seg BAI ; Bon-Hoon KOO ; Eun-Jin CHEON ; Seokho YUN ; So Hye JO ; Byoungyoung GU
Journal of Korean Medical Science 2025;40(9):e20-
Background:
Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia.
Methods:
A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split.
Results:
The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts.
Conclusion
The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.
6.Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology
Hye-Geum KIM ; Dai-Seg BAI ; Bon-Hoon KOO ; Eun-Jin CHEON ; Seokho YUN ; So Hye JO ; Byoungyoung GU
Journal of Korean Medical Science 2025;40(9):e20-
Background:
Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia.
Methods:
A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split.
Results:
The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts.
Conclusion
The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.
8.Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology
Hye-Geum KIM ; Dai-Seg BAI ; Bon-Hoon KOO ; Eun-Jin CHEON ; Seokho YUN ; So Hye JO ; Byoungyoung GU
Journal of Korean Medical Science 2025;40(9):e20-
Background:
Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia.
Methods:
A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split.
Results:
The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts.
Conclusion
The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.
10.Implementation of Pharmaceutical Care Services During the COVID-19 Pandemic Worldwide
So Yeon LEE ; Seunghyun CHEON ; Hye Won PARK ; Sang Hyeon OH ; Jee-Eun CHUNG ; Sook Hee AN
Korean Journal of Clinical Pharmacy 2024;34(4):242-251
Background:
This study sought to research the implementation of pharmaceutical care services and review the pharmaceutical care services used for coronavirus disease 2019 (COVID-19) prevention, diagnosis, therapy, and vaccination during the COVID-19 pandemic.
Methods:
All articles reporting pharmacists’ implementation of pharmaceutical care services during the COVID-19 pandemic were comprehensively searched in PubMed/Medline, Embase, and the Cochrane Library databases up toJuly 7, 2021, then included in this study. Twenty-four items of pharmaceutical care services were classified into the following 5categories: patient evaluation and monitoring, clinical decision support, compounding/dispensing/administration, patient consultation and education, and drug-related policy research and development.
Results:
A total of 674 articles from 100 countrieswere included, with the United States of America being the most frequently studied country. Across the 5 classified categories,compounding/dispensing/administration was most frequently examined (28.9%), followed by patient consultation and education (25.2%). Among the 24 items of pharmaceutical care services, medicine supply management was most frequently reported on (11.4%), followed by patient consultations (11.0%). The primary implemented pharmaceutical care services for COVID-19 prevention, diagnosis, therapy, and vaccination were public health education, COVID-19 testing services, medicine supply management, and vaccination, respectively.
Conclusion
Pharmacists have implemented diverse pharmaceutical care services for COVID-19 prevention, diagnosis, therapy, and vaccination globally. Further studies should be conducted to determine the correlation between the characteristics of healthcare accessibility in a country and the implemented pharmaceutical care services for COVID-19.

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