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Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using an SVM classifier
View ORCID ProfileUtkarsh Tripathi, Liron Mizrahi, View ORCID ProfileMartin Alda, View ORCID ProfileGregory Falkovich, View ORCID ProfileShani Stern
doi: https://doi.org/10.1101/2022.04.04.486856
Utkarsh Tripathi
1Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel
Liron Mizrahi
1Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel
Martin Alda
2Department of Psychiatry, Dalhousie University, 5909 Veterans’ Memorial Lane, Halifax, NS B3H 2E2, Canada
Gregory Falkovich
3Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, ISRAEL
Shani Stern
1Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa 3498838, Israel
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Posted April 05, 2022.
Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using an SVM classifier
Utkarsh Tripathi, Liron Mizrahi, Martin Alda, Gregory Falkovich, Shani Stern
bioRxiv 2022.04.04.486856; doi: https://doi.org/10.1101/2022.04.04.486856
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