Longitudinal Study-Based Dementia Prediction for Public Health

Abstract

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly.

Tekoälyn hyödyntäminen terveydenhuollossa terveysriskien ja riskitekijöiden tunnistamiseksi ja ennustamiseksi

“…Tekoälyn menetelmiä on hyödynnetty esimerkiksi aivoaneurysmaan [17], osteoporoosiin [18], mielenterveyden häiriöihin [19,20], dementiaan [21], runsaaseen virtsahappopitoisuuteen [22] ja sydänsairauksiin [23][24][25] liittyvien riskitekijöiden tunnistamiseksi. Lisäksi tekoälyn menetelmiä on hyödynnetty yleisemmin sairauksiin liittyvien riskitekijöiden [26] ja terveydentilojen tunnistamiseksi [27,28].…”

“…Taulukossa 1 on kuvattu mihin sairauksiin tai terveydentiloihin liittyen terveysriskejä ja riskitekijöitä on tunnistettu tekoälyn avulla. [21]. Tuarob tutkimusryhmineen [20] puolestaan keräsi mielenterveyden riskitekijöiden tunnistamiseksi tietoa kolmeen eri otteeseen kahdeksan kuukauden aikana.…”

“…Rakenteisessa muodossa olevia tietoja on tutkimuksia varten poimittu erilaisista mittaustuloksista [18,22,24,26] ja kyselyistä [18,20]. Rakenteista tietoa on saatu myös lääkityksistä [23,27], diagnooseista [17,19,21,24,27] ja toimenpiteistä [17,27]. Potilastietojärjestelmissä on kuitenkin edelleen paljon tietoa vain narratiivisessa muodossa [17].…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…ML applied to electronic health records was demonstrated to predict suicide risk with an accuracy similar to clinician assessment [268,271], as well as predict dementia and its risk factors with high accuracy [272]. Research has also investigated the use of ML with clinical data to improve variable selection in epidemiological data analysis [273], and to better understand the relationship between complex risk factors for mental health conditions such as depression [274].…”

“…Anxiety SVM [276], Linear discriminant analysis [276], RF [276] Electronic Health Records [276] Cognitive Distortions DT [277], Regression [277], NB [277], NN [277], kNN [277], RELIEF [277] Social Media [277] Dementia SVM [272] Electronic Health Records [272] Depression DT [278], Gradient boosting [279], kNN [278], LIWC [280], LDA [280], Linear discriminant analysis [276], NB [278], NN [274], RF [276], Regression [274], SVM [276,278] Electronic Health Records [276], Social Media [278,280], Survey [274,279] Grief LIWC [266], SVM [266] Social Media [266] Mental Health Service Usage…”