Machine learning on encephalographic activity may predict opioid analgesia-Reference-Cited by-同舟云学术

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Machine learning on encephalographic activity may predict opioid analgesia

Published:2015-06-11 Issue:10 Volume:19 Page:1552-1561
ISSN:1090-3801
Container-title:European Journal of Pain
language:en
Short-container-title:Eur J Pain

Author:

Gram M.¹,Graversen C.¹,Olesen A.E.¹²,Drewes A.M.¹³

Affiliation:

1. Mech-Sense; Department of Gastroenterology and Hepatology; Aalborg University Hospital; Denmark

2. Department of Drug Design and Pharmacology; Faculty of Health and Medical Sciences; University of Copenhagen; Denmark

3. Clinical Institute; Aalborg University Hospital; Denmark

Funder

Strategiske Forskningsråd

Publisher

Wiley

Subject

Anesthesiology and Pain Medicine

Reference38 articles.

1. Personalized medicine-one size fits one: Tailoring pain therapy to individuals' needs;Ahmedzai;J Pain Palliat Care Pharmacother,2013

2. Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images;Auffarth;Adv Data Mining Appl,2010

3. Pre-stimulus alpha power affects vertex N2-P2 potentials evoked by noxious stimuli;Babiloni;Brain Res Bull,2008

4. Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder;Bares;Eur Psychiatry,2008

5. Conditional likelihood maximisation: A unifying framework for information theoretic feature selection;Brown;J Mach Learn Res,2012

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