Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models - Igarashi, Toshiharu; Umeda-Kameyama, Yumi; Kojima, Taro; Akishita, Masahiro; Nihei, Misato (Frontiers in Medicine, 2023)
In this paper, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the feasibility of the developed interview items and the accuracy of the natural language processing model, we recruited participants with the approval of the University of Tokyo Hospital and obtained the cooperation of 29 participants (7 men and 22 women) aged 72 to 91 years. Based on the MMSE results, a multilevel classification model was created to classify the three groups, and a binary classification model to sort the two groups. For each of these models, we tested whether the accuracy would improve when text augmentation was performed. The percentage of correct answers in the multi-level classification results for the test data was 0.405 without augmentation and 0.991 with augmentation. The percentage of correct answers in the test data in the results of the binary classification without augmentation was 0.488 for the moderate dementia and mild dementia groups, 0.767 for the moderate dementia and MCI groups, and 0.700 for the mild dementia and MCI groups. This suggests that without augmentation, it was possible to classify the moderate dementia group (or mild dementia group) and the MCI group with an accuracy of over 70%; however, it was difficult to classify the moderate dementia group and the mild dementia group. In contrast, the percentage of correct answers in the test data in the augmented binary classification results was 0.972 for the moderate dementia group and the mild dementia group, 0.996 for the moderate dementia group and the MCI group, and 0.985 for the mild dementia group and the MCI group.