ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2021
Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Causal Explanation from Mild Cognitive Impairment Progression using Graph Neural Networks.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Characterizing the progression from mild cognitive impairment to dementia: a network analysis of longitudinal clinical visits.BMC medical informatics and decision making · 2024Article
- Dementia Prediction in Older Adults Using Sex-Specific Health Trajectory Clustering.Studies in health technology and informatics · 2024Article
- Dementia risk analysis using temporal event modeling on a large real-world dataset.Scientific reports · 2023Article
- Detection of Dementia Signals from Longitudinal Clinical Visits Using One-Class Classification.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
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Authors and funding
7 authors.
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Abstract
more than 44 million people have been diagnosed with dementia worldwide, and this number is estimated to triple by next three decades. Given this increasing trend of older adults with cognitive impairment (CI; dementia and mild cognitive impairment) and its significant underdiagnosis, early identification of CI and understanding its progression is a critical step towards a better quality of life for the aging population. Early alert of individual health changes could facilitate better ways for clinicians to diagnose CI in its early stages and come up with more effective treatment plans. However, there is a lack of approaches to characterize patient health conditions accounting for temporal information in an unsupervised manner. Limited CI cases and its costly ascertainment in clinical settings also make unsupervised learning more promising in CI research. In this paper, a streaming clustering model was used to determine distinct patterns of older adults' health changes from their clinical visits in Mayo Clinic Study of Aging. The streaming clustering was also examined to study its ability to generate early alerts for potential incidents of CI. Our analysis demonstrated that temporal characteristics incorporated in a streaming clustering model has a promising potential to increase power in predicting CI.
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Registered trials
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