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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Early detection and personalized medicine: Future strategies against Alzheimer's disease
Antonio Di Meco1, Robert Vassar1
1Department of Neurology, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Abstract:
Alzheimer's disease (AD) is the leading cause of dementia and sixth cause of death in elderly adults. AD poses a huge economic burden on society and constitutes an unprecedented challenge for caregivers and families affected. Aging of the population is projected to drastically aggravate the situation in the near future. To date, no therapy is available to prevent or ameliorate the disease. Moreover, several clinical trials for promising therapeutic agents have failed. Lack of supporting biomarker data for pre-symptomatic enrollment and inaccurate stratification of patients based on genetic heterogeneity appear to be contributing factors to this lack of success. Recently, the treatment of cancer has seen enormous advances based on the personalized genetics and biomarkers of the individual patient, forming the foundation of precision medicine for cancer. Likewise, technological progress in AD biomarker research promises the availability of reliable assays for pathology staging on a routine basis relatively soon. Moreover, tremendous achievements in AD genetics and high-throughput genotyping technology allow identification of predisposing risk alleles accurately and on a large scale. Finally, availability of electronic health records (EHR) promises the opportunity to integrate biomarker, genomic and clinical data efficiently. Together, these advances will form the basis of precision medicine for AD.
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