Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

Research output: Contribution to journalJournal articlepeer-review

  1. Maternal pregnancy-related infections and autism spectrum disorder-the genetic perspective

    Research output: Contribution to journalJournal articlepeer-review

  2. Acute and long-term effects of psilocybin on energy balance and feeding behavior in mice

    Research output: Contribution to journalJournal articlepeer-review

  3. Systemic DNA and RNA damage from oxidation after serotonergic treatment of unipolar depression

    Research output: Contribution to journalJournal articlepeer-review

  1. Diabetes, sleep disorders and risk of depression - A Danish register-based cohort study

    Research output: Contribution to journalJournal articlepeer-review

  2. Kommentar i udsendelse om Psykiatri på TV2 Play

    Research output: Non-textual formSound/Visual production (digital)Communication

View graph of relations

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

Original languageEnglish
Article number276
JournalTranslational psychiatry
Volume10
Issue number1
ISSN2158-3188
DOIs
Publication statusPublished - 10 Aug 2020

ID: 60645069