Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches
| Autor: | Courtney Beard, Christian A. Webb, Zachary D. Cohen, Marie J. C. Forgeard, Thröstur Björgvinsson, Andrew D. Peckham |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Hospitals Psychiatric Male 050103 clinical psychology Adolescent medicine.medical_treatment Sample (statistics) PsycINFO Machine learning computer.software_genre Article law.invention Machine Learning Young Adult Randomized controlled trial law medicine Psychiatric hospital Humans 0501 psychology and cognitive sciences Precision Medicine Radiation treatment planning Depression (differential diagnoses) Aged Depressive Disorder Cognitive Behavioral Therapy business.industry 05 social sciences Reproducibility of Results Middle Aged Prognosis Outcome (probability) Antidepressive Agents Cognitive behavioral therapy Psychiatry and Mental health Clinical Psychology Treatment Outcome Female Artificial intelligence Psychology business computer |
| Zdroj: | J Consult Clin Psychol |
| ISSN: | 1939-2117 |
| Popis: | OBJECTIVE Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of study clinicians. The extent to which findings from such studies generalize to naturalistic psychiatric settings is unclear. This study sought to predict depression outcomes for patients seeking treatment within an intensive psychiatric hospital setting and while comparing the performance of a range of machine learning approaches. METHOD Depressed patients (N = 484; ages 18-72; 89% White) receiving treatment within a psychiatric partial hospital program delivering pharmacotherapy and cognitive behavioral therapy were split into a training sample and holdout sample. First, within the training sample, 51 pretreatment variables were submitted to 13 machine learning algorithms to predict, via cross-validation, posttreatment Patient Health Questionnaire-9 depression scores. Second, the best performing modeling approach (lowest mean squared error; MSE) from the training sample was selected to predict outcome in the holdout sample. RESULTS The best performing model in the training sample was elastic net regularization (ENR; MSE = 20.49, R2 = .28), which had comparable performance in the holdout sample (MSE = 11.26; R2 = .38). There were 14 pretreatment variables that predicted outcome. To demonstrate the translation of an ENR model to personalized prediction of treatment outcome, a patient-specific prognosis calculator is presented. CONCLUSIONS Informed by pretreatment patient characteristics, such predictive models could be used to communicate prognosis to clinicians and to guide treatment planning. Identified predictors of poor prognosis may suggest important targets for intervention. (PsycINFO Database Record (c) 2019 APA, all rights reserved). |
| Databáze: | OpenAIRE |
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