Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning
Abstract Background Alzheimer’s disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognitio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-12-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01693-6 |