The promise of machine learning in predicting treatment outcomes in psychiatry

Chekroud, Adam M., Bondar, Julia, Delgadillo, Jaime, Doherty, Gavin, Wasil, Akash, Fokkema, Marjolein, Cohen, Zachary, Belgrave, Danielle +4 more·2021·World Psychiatry

Cites (221)

Items 201–221 of 221

  1. Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems
    María José Gacto, Jose Manuel Soto-Hidalgo, Jesús Alcalá‐Fdez, Rafael Alcalá · 2019 · IEEE Access
  2. Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models
    Michael C. Hughes, Melanie F. Pradier, Andrew Slavin Ross, Thomas H. McCoy, Roy H. Perlis, Finale Doshi‐Velez · 2020 · JAMA Network Open
  3. Mapping differential responses to cognitive training using machine learning
    Joseph P. Rennie, Mengya Zhang, Erin Hawkins, Joe Bathelt, Duncan Edward Astle · 2019 · Developmental Science
  4. Symbiotic Interaction
    Anna Spagnolli, Jonathan Freeman, Luciano Gamberini, Giulio Jacucci · 2014 · Lecture notes in computer science
  5. A retrospective observational analysis to identify patient and treatment-related predictors of outcomes in a community mental health programme
    Stuart A. Green, Emmi Honeybourne, Sylvia R. Chalkley, Alan J Poots, Thomas M. Woodcock, Geraint J. Price, Derek Bell, John Green · 2015 · BMJ Open
  6. Gene Variants at Loci Related to Blood Pressure Account for Variation in Response to Antihypertensive Drugs Between Black and White Individuals
    Raquel Iniesta, Desmond Campbell, Cristina Venturini, Luca Faconti, Sonal Singh, Marguerite R. Irvin, Rhonda M. Cooper‐DeHoff, Julie A. Johnson, Stephen T. Turner, Donna K. Arnett et al. · 2019 · Hypertension
  7. Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis
    Eva Tolmeijer, Veena Kumari, EMMANUELLE R. PETERS, Steven Williams, Liam Mason · 2018 · NeuroImage Clinical
  8. Predicting Response to the Antidepressant Bupropion Using Pretreatment fMRI
    Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher, Cooper Mellema, Madhukar H. Trivedi, Albert Montillo · 2019 · Lecture notes in computer science
  9. Oxford Handbook of Law, Regulation and Technology
    Eloise Scotford, Roger Brownsword, Karen Yeung · 2017 · Research Portal (King's College London)
  10. The Leeds Risk Index: Field-Test of a Stratified Psychological Treatment Selection Algorithm
    Jaime Delgadillo, Sarah Appleby, Sarah Booth, Georgie Burnett, Amy Carey, Laura Edmeade, Stephen Green, Pat Griffin, Emma Johnson, Robert Jones et al. · 2020 · Psychotherapy and Psychosomatics
  11. Predicting therapy success for treatment as usual and blended treatment in the domain of depression
    Ward van Breda, Vincent Bremer, Dennis Becker, Mark Hoogendoorn, Burkhardt Funk, Jeroen Ruwaard, Heleen M. Riper · 2017 · Internet Interventions
  12. Potential predictors of depressive relapse following repetitive Transcranial Magnetic Stimulation: A systematic review
    Aleksandra Miljevic, Neil Wayne Bailey, Sally E. Herring, Paul Bernard Fitzgerald · 2019 · Journal of Affective Disorders
  13. Precision ECT for major depressive disorder: A review of clinical factors, laboratory, and physiologic biomarkers as predictors of response and remission
    Zhixing Yao, William Vaughn McCall, Norah Essali, Ethan Wohl, Carmen Parker, Peter B. Rosenquist, Nagy A. Youssef · 2019 · Personalized Medicine in Psychiatry
  14. Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery
    Camilla L. Nord · 2021 · Current Behavioral Neuroscience Reports
  15. Comment on Starke et al.: ‘Computing schizophrenia: ethical challenges for machine learning in psychiatry’: from machine learning to student learning: pedagogical challenges for psychiatry
    Christophe Gauld, Jean‐Arthur Micoulaud‐Franchi, Guillaume Dumas · 2020 · Psychological Medicine
  16. The Genomics of Electroconvulsive Therapy International Consortium (GenECT-ic)
    Bernhard Theodor Baune, Takahiro Soda, Patrick F. Sullivan, Peter P. Zandi · 2019 · The Lancet Psychiatry
  17. Evolving Relationship between Humans and Machines
    Anna Trujillo, Irene M. Gregory, Kasey A. Ackerman · 2019 · IFAC-PapersOnLine
  18. Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data
    Vincent Bremer, Burkhardt Funk, Heleen M. Riper · 2019 · Depression Research and Treatment
  19. Clinical risk prediction in schizophrenia
    Stephen M. Lawrie · 2014 · The Lancet Psychiatry
  20. The Early Roots of Statistical Learning in the Psychometric Literature: A review and two new results
    Mark de Rooij, Bunga Citra Pratiwi, Marjolein Fokkema, Elise Dusseldorp, Henk Kelderman · 2019 · arXiv (Cornell University)
  21. Corrigendum to “Pretreatment biomarkers predicting PTSD psychotherapy outcomes: A systematic review” [Neurosci. Biobehav. Rev. 75 (2017) 140–156]
    Peter J. Colvonen, Lisa H. Glassman, Laura D. Crocker, Melissa M. Buttner, Henry Orff, Dawn M. Schiehser, Sonya B. Norman, Niloofar Afari · 2018 · Neuroscience & Biobehavioral Reviews