Precision Medicine: Academic dreaming or clinical reality?

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Tytuł:: Precision Medicine: Academic dreaming or clinical reality?
Autorzy:: Josephson CB; Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.; Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.; O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.; Centre for Health Informatics, University of Calgary, Calgary, AB, Canada.
Wiebe S; Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.; Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.; O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.; Clinical Research Unit, University of Calgary, Calgary, AB, Canada.
Źródło:: Epilepsia [Epilepsia] 2021 Mar; Vol. 62 Suppl 2, pp. S78-S89. Date of Electronic Publication: 2020 Nov 17.
Typ publikacji:: Journal Article; Review
Język:: English
Imprint Name(s):: Publication: Malden, MA : Blackwell Science
Original Publication: Copenhagen : Munskgaard
MeSH Terms:: Algorithms*
Data Analysis*
Electronic Health Records*/trends
Data Collection/*methods
Epilepsy/*therapy
Precision Medicine/*methods
Data Collection/trends ; Epilepsy/diagnosis ; Humans ; Precision Medicine/trends
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Contributed Indexing:: Keywords: big data; epilepsy; machine learning; personalized medicine; precision medicine
Entry Date(s):: Date Created: 20201118 Date Completed: 20210921 Latest Revision: 20210921
Update Code:: 20240105
DOI:: 10.1111/epi.16739
PMID:: 33205406
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Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.
(© 2020 International League Against Epilepsy.)

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