Digital Library[ Search Result ]
Integrating Domain Knowledge with Graph Convolution based on a Semantic Network for Elderly Depression Prediction
Seok-Jun Bu, Kyoung-Won Park, Sung-Bae Cho
http://doi.org/10.5626/JOK.2023.50.3.243
Depression in the elderly is a global problem that causes 300 million patients and 800,000 suicides every year, so it is critical to detect early daily activity patterns closely related to mobility. Although a graph-convolution neural network based on sensing logs has been promising, it is required to represent high-level behaviors extracted from complex sensing information sequences. In this paper, a semantic network that structuralizes the daily activity patterns of the elderly was constructed using additional domain knowledge, and a graph convolution model was proposed for complementary uses of low-level sensing log graphs. Cross-validation with 800 hours of data from 69 senior citizens provided by DNX, Inc. revealed improved prediction performance for the suggested strategy compared to the most recent deep learning model. In particular, the inference of a semantic network was justified by a graph convolution model by showing a performance improvement of 28.86% compared with the conventional model.
Usability Assessment of FHIR-based Geriatric Depression Scale Questionnaire Using Chatbot
http://doi.org/10.5626/JOK.2020.47.7.650
As Korea enters the aging society, the interest in, and importance of the elderly are increasing. In particular, the depression of the elderly is an important issue to be addressed. To this end, latency delays are among the most common complaints about those who seek medical examination or to see a doctor. Also, if patients move and are thus are sent to a different hospital because of a change of residence or personal reasons, they may undergo the same examination. In this case, fatigue and economic burden are placed upon the patient because of the re-examination. In this study, we have implemented the chatbot for the Geriatric Depression Scale Questionnaire based on the Fast Healthcare Interoperability Resource, an international health information exchange standard. Unlike the existing paper questionnaire, it has interoperable questionnaire information, and the user’s perceived usability was examined through the evaluation of usability.
Diagnostic and Therapeutic Model for Korean Major Depressive Disorder Using Multi-Modal Data
Yonghwa Choi, Aram Kim, Minji Jeon, Sunkyu Kim, Kyu-Man Han, Eunsoo Won, Byung-Joo Ham, Jaewoo Kang
http://doi.org/10.5626/JOK.2019.46.1.71
Depression is one of the most common mental illnesses in the modern society, and it increases the social burden due to repeated recurrences. However, since there are many pre-disposing factors that cause depression, there is need to develop a machine-learning model that examine these factors effectively. In this paper, we propose a model that can diagnose depression and predict the degree of antidepressant response using four multi modal data including basic information, MRI, genetics, and cognitive test. The model achieved 0.923 AUROC score for diagnosis and 0.08 MSE for prediction of antidepressant response. In addition, the results of the proposed model were quantitatively analyzed, and it confirmed that accurate diagnosis and drug response prediction are possible when the patient’s data is added. Qualitative analysis was also conducted to provide new hypotheses as well as findings on the main factors causing depression.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr