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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach.

Micah CearnsAzmeraw T AmareKlaus Oliver SchubertAnbupalam ThalamuthuJoseph FrankFabian StreitMazda AdliNirmala AkulaKazufumi AkiyamaRaffaella ArdauBárbara AriasJean-Michel AubryLena BacklundAbesh Kumar BhattacharjeeFrank BellivierAntonio BenabarreSusanne BengesserJoanna M BiernackaArmin BirnerClara Brichant-PetitjeanPablo CervantesHsi-Chung ChenCaterina ChillottiSven CichonCristiana CruceanuPiotr M CzerskiNina DalknerAlexandre DayerFranziska DegenhardtMaria Del ZompoJ Raymond DePauloBruno EtainPeter FalkaiAndreas J ForstnerLouise FrisenMark A FryeJanice M FullertonSébastien GardJulie S GarnhamFernando S GoesMaria Grigoroiu-SerbanescuPaul GrofRyota HashimotoJoanna HauserUrs HeilbronnerStefan HermsPer HoffmannAndrea HofmannLiping HouYi-Hsiang HsuStephane JamainEsther JiménezJean-Pierre KahnLayla KassemPo-Hsiu KuoTadafumi KatoJohn KelsoeSarah Kittel-SchneiderSebastian KliwickiBarbara KönigIchiro KusumiGonzalo LajeMikael LandénCatharina LavebrattMarion LeboyerSusan G LeckbandMario Majnull nullMirko ManchiaLina MartinssonMichael J McCarthySusan McElroyFrancesc ColomMarina MitjansFrancis M MondimorePalmiero MonteleoneCaroline M NievergeltMarkus M NöthenTomas NovákClaire O'DonovanNorio OzakiVincent MillischerSergi PapiolAndrea PfennigClaudia PisanuJames B PotashAndreas ReifEva ReininghausGuy A RouleauJanusz K RybakowskiMartin SchallingPeter R SchofieldBarbara W SchweizerGiovanni SeverinoTatyana ShekhtmanPaul D ShillingKatzutaka ShimodaChristian SimhandlClaire M SlaneyAlessio SquassinaThomas StammPavla StopkovaFasil Tekola-AyeleAlfonso TortorellaGustavo TureckiJulia VeehEduard VietaStephanie H WittGloria RobertsPeter P ZandiMartin AldaMichael BauerFrancis J McMahonPhilip B MitchellThomas G SchulzeMarcella RietschelScott R ClarkBernhard T Baune
Published in: The British journal of psychiatry : the journal of mental science (2022)
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Keyphrases
  • machine learning
  • bipolar disorder
  • end stage renal disease
  • newly diagnosed
  • ejection fraction
  • deep learning
  • high resolution
  • mass spectrometry
  • high speed