Name
Abstract | ||
|---|---|---|
The WHO identified depressive disorder as one of the three major diseases in the 21st century and studies have shown that patients with depression are more likely than non depression to have dementia in the future. However, although there are many related studies that point out that depressive disorder is one of the important factor of dementia, however, these findings are not consistent. In addition, there has been no study of evidence-based construction of dementia prediction model of depressive disorder patients for clinical practice. Therefore, this study will use supervised learning techniques to construct a follow-up dementia prediction model for depressive disorder patients to assist depressive disorder patients and their medical staffs to predict his/her possible risk of suffering from dementia, and then develop early intervention and prevention measures. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMLC48188.2019.8949191 | PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC) |
Keywords | Field | DocType |
Depressive Disorder, Dementia, Disease Severity, Machine Learning | Computer science,Clinical Practice,Supervised learning,Psychiatry,Artificial intelligence,Machine learning,Dementia | Conference |
ISSN | Citations | PageRank |
2160-133X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsiao-Ting Tseng | 1 | 0 | 0.34 |
| Hsiao-Chi Li | 2 | 0 | 0.34 |
| Chia-Lun Lo | 3 | 0 | 0.34 |
| Tai-Hsiang Shen | 4 | 0 | 0.34 |
| Shu-Chiung Lin | 5 | 0 | 0.34 |