Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

• Published in: Journal of Medical Internet Research Vol. 25; no. 1; p. e41858
• Main Authors: Huang, Shih-Tsung, Hsiao, Fei-Yuan, Tsai, Tsung-Hsien, Chen, Pei-Jung, Peng, Li-Ning, Chen, Liang-Kung
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications Inc 26.07.2023; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Aged; Algorithms; Beneficiaries; Cerebrovascular disease; Cerebrovascular Disorders; Classification; Cluster Analysis; Clustering; Cohort analysis; Cohort Studies; Comorbidity; Computer applications to medicine. Medical informatics; Data visualization; Datasets; Dementia; Disease prevention; Humans; Hypertension; Hypotheses; Infections; Locomotion; Longitudinal Studies; Machine Learning; Medical coding; Mobility; National health insurance; Neurological disorders; Older people; Optimization; Original Paper; Population; Prediction models; Psychotropic drugs; Public aspects of medicine; R858-859.7; RA1-1270; Regression analysis; Risk factors; Sequences; Support vector machines; Visualization; Taiwan; development; infection; mobility; disease; data; neuropsychiatric; data set; cardiovascular; machine learning; symptoms; condition; mental conditions; dementia; cluster analysis

Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident...