Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier

TY - JOUR

T1 - Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier

AU - Tripathi, Utkarsh

AU - Mizrahi, Liron

AU - Alda, Martin

AU - Falkovich, Gregory

AU - Stern, Shani

N1 - Funding Information: This material is based upon work supported by the Zuckerman STEM Leadership Program, ISF grant 1994/21, and ISF grant 3252/21 for Shani Stern and Simons foundation (662962), EU Horizon 2020 (83937, 873028) and BSF (2018033, 2020765) for Gregory Falkovich. The clinical part of this study was supported by grants from Genome Atlantic/ Reserach Nova Scotia (RPPP) and from ERA PerMed (PLOT‐BD) to Martin Alda. Funding Information: Zuckerman STEM Leadership Program for Shani Stern, ISF grant 1994/21, ISF grant 3252/21 for Shani Stern, Simons foundation (662962), EU Horizon 2020 (83937, 873028) and BSF (2018033, 2020765) for Gregory Falkovich. Publisher Copyright: © 2022 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.

PY - 2023/3

Y1 - 2023/3

UR - http://www.scopus.com/inward/record.url?scp=85144178695&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85144178695&partnerID=8YFLogxK

U2 - 10.1111/bdi.13282

DO - 10.1111/bdi.13282

M3 - Article

C2 - 36479788

AN - SCOPUS:85144178695

SN - 1398-5647

VL - 25

SP - 110

EP - 127

JO - Bipolar Disorders

JF - Bipolar Disorders

IS - 2

ER -