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- W2340477513 abstract "The prevalence of Alzheimer’s disease (AD) and other forms of dementia is increasing with the aging population, both in the United States and around the globe. The inability to cure these conditions results in prolonged and expensive medical care. Early detection is critical to potentially postpone symptoms and to prepare both healthcare providers and families for subjects’ future needs. Current detection methods are typically costly or unreliable, and much stands to benefit from improved recognition of early AD markers. Electronic patient records provide the potential for computational analysis and prediction of complex diseases like AD. Whereas prior work on this problem has focused on structured data (e.g. test results) alone, this study integrates structured and unstructured (e.g. clinical notes) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI)* for classification of subjects’ dementia status. Prediction based on unstructured data alone performs with similar accuracy compared to structured data, and integration of the two provides performance improvements over either in isolation. In addition, we provide insights into which structured features were more useful for classification of AD, supporting previously observed trends, while also highlighting the potential for computational methods to discover new early markers." @default.
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- W2340477513 date "2015-01-01" @default.
- W2340477513 modified "2023-09-26" @default.
- W2340477513 title "Inference from Structred and Unstructured Electronic Medical Data for Early Dementia Detection" @default.
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