167 related articles for article (PubMed ID: 34509784)
1. Exploring machine learning to predict depressive relapses of bipolar disorder patients.
Rotenberg LS; Borges-Júnior RG; Lafer B; Salvini R; Dias RDS
J Affect Disord; 2021 Dec; 295():681-687. PubMed ID: 34509784
[TBL] [Abstract][Full Text] [Related]
2. Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers.
Zhu T; Liu X; Wang J; Kou R; Hu Y; Yuan M; Yuan C; Luo L; Zhang W
Comput Methods Programs Biomed; 2023 Oct; 240():107723. PubMed ID: 37480646
[TBL] [Abstract][Full Text] [Related]
3. Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.
Wu MJ; Passos IC; Bauer IE; Lavagnino L; Cao B; Zunta-Soares GB; Kapczinski F; Mwangi B; Soares JC
J Affect Disord; 2016 Mar; 192():219-25. PubMed ID: 26748737
[TBL] [Abstract][Full Text] [Related]
4. A mania-related memory bias is associated with risk for relapse in bipolar disorder.
Meyer TD; Hautzinger M; Bauer IE
J Affect Disord; 2018 Aug; 235():557-564. PubMed ID: 29698917
[TBL] [Abstract][Full Text] [Related]
5. Cognitive deficits in bipolar disorders: Implications for emotion.
Lima IMM; Peckham AD; Johnson SL
Clin Psychol Rev; 2018 Feb; 59():126-136. PubMed ID: 29195773
[TBL] [Abstract][Full Text] [Related]
6. The influence of affective temperaments and psychopathological traits on the definition of bipolar disorder subtypes: a study on bipolar I Italian national sample.
Perugi G; Toni C; Maremmani I; Tusini G; Ramacciotti S; Madia A; Fornaro M; Akiskal HS
J Affect Disord; 2012 Jan; 136(1-2):e41-e49. PubMed ID: 20129674
[TBL] [Abstract][Full Text] [Related]
7. The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review.
Jan Z; Ai-Ansari N; Mousa O; Abd-Alrazaq A; Ahmed A; Alam T; Househ M
J Med Internet Res; 2021 Nov; 23(11):e29749. PubMed ID: 34806996
[TBL] [Abstract][Full Text] [Related]
8. Circadian Rhythm Sleep-Wake Disorders Predict Shorter Time to Relapse of Mood Episodes in Euthymic Patients With Bipolar Disorder: A Prospective 48-Week Study.
Takaesu Y; Inoue Y; Ono K; Murakoshi A; Futenma K; Komada Y; Inoue T
J Clin Psychiatry; 2018; 79(1):. PubMed ID: 29286593
[TBL] [Abstract][Full Text] [Related]
9. Borderline Personality Features in Inpatients with Bipolar Disorder: Impact on Course and Machine Learning Model Use to Predict Rapid Readmission.
Salem H; Ruiz A; Hernandez S; Wahid K; Cao F; Karnes B; Beasley S; Sanches M; Ashtari E; Pigott T
J Psychiatr Pract; 2019 Jul; 25(4):279-289. PubMed ID: 31291208
[TBL] [Abstract][Full Text] [Related]
10. Machine learning applied to prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review.
Amanollahi M; Jameie M; Looha MA; Basti FA; Cattarinussi G; Moghaddam HS; Di Camillo F; Akhondzadeh S; Pigoni A; Sambataro F; Brambilla P; Delvecchio G
J Affect Disord; 2024 Jun; ():. PubMed ID: 38908556
[TBL] [Abstract][Full Text] [Related]
11. Sleep, residual mood symptoms, and time to relapse in recovered patients with bipolar disorder.
Cretu JB; Culver JL; Goffin KC; Shah S; Ketter TA
J Affect Disord; 2016 Jan; 190():162-166. PubMed ID: 26519636
[TBL] [Abstract][Full Text] [Related]
12. Identification and initial validation of empirically derived bipolar symptom states from a large longitudinal dataset: an application of hidden Markov modeling to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study.
Prisciandaro JJ; Tolliver BK; DeSantis SM
Psychol Med; 2019 May; 49(7):1102-1108. PubMed ID: 30153871
[TBL] [Abstract][Full Text] [Related]
13. Bipolar II disorder : epidemiology, diagnosis and management.
Benazzi F
CNS Drugs; 2007; 21(9):727-40. PubMed ID: 17696573
[TBL] [Abstract][Full Text] [Related]
14. Affective temperaments are associated with specific clusters of symptoms and psychopathology: a cross-sectional study on bipolar disorder inpatients in acute manic, mixed, or depressive relapse.
Iasevoli F; Valchera A; Di Giovambattista E; Marconi M; Rapagnani MP; De Berardis D; Martinotti G; Fornaro M; Mazza M; Tomasetti C; Buonaguro EF; Di Giannantonio M; Perugi G; de Bartolomeis A
J Affect Disord; 2013 Nov; 151(2):540-550. PubMed ID: 23856282
[TBL] [Abstract][Full Text] [Related]
15. Does psychomotor agitation in major depressive episodes indicate bipolarity? Evidence from the Zurich Study.
Angst J; Gamma A; Benazzi F; Ajdacic V; Rössler W
Eur Arch Psychiatry Clin Neurosci; 2009 Feb; 259(1):55-63. PubMed ID: 18806921
[TBL] [Abstract][Full Text] [Related]
16. Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.
Belizario GO; Junior RGB; Salvini R; Lafer B; Dias RDS
J Affect Disord; 2019 Feb; 245():279-282. PubMed ID: 30419527
[TBL] [Abstract][Full Text] [Related]
17. Application of machine learning in diagnostic value of mRNAs for bipolar disorder.
Wu X; Zhu L; Zhao Z; Xu B; Yang J; Long J; Su L
Nord J Psychiatry; 2022 Feb; 76(2):81-88. PubMed ID: 34156910
[TBL] [Abstract][Full Text] [Related]
18. Antidepressant-associated mania or hypomania: a comparison with personality and bipolarity features of bipolar I disorder.
Saatcioglu O; Erim R; Tomruk N; Oral T; Alpay N
J Affect Disord; 2011 Nov; 134(1-3):85-90. PubMed ID: 21632117
[TBL] [Abstract][Full Text] [Related]
19. A Pharmacologic Algorithm for Youth Who Are at High Risk for Bipolar Disorder.
Schneck CD; Chang KD; Singh MK; DelBello MP; Miklowitz DJ
J Child Adolesc Psychopharmacol; 2017 Nov; 27(9):796-805. PubMed ID: 28731778
[TBL] [Abstract][Full Text] [Related]
20. Five-year outcome of bipolar I and II disorders: findings of the Jorvi Bipolar Study.
Pallaskorpi S; Suominen K; Ketokivi M; Mantere O; Arvilommi P; Valtonen H; Leppämäki S; Isometsä E
Bipolar Disord; 2015 Jun; 17(4):363-74. PubMed ID: 25726951
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]