Development of a computerized adaptive diagnostic screening tool for psychosis

Robert D. Gibbons*, Ishanu Chattopadhyay, Herbert Y. Meltzer, John M. Kane, Daniel Guinart

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We develop a two-stage diagnostic classification system for psychotic disorders using an extremely randomized trees machine learning algorithm. Item bank was developed from clinician-rated items drawn from an inpatient and outpatient sample. In stage 1, we differentiate schizophrenia and schizoaffective disorder from depression and bipolar disorder (with psychosis). In stage 2 we differentiate schizophrenia from schizoaffective disorder. Out of sample classification accuracy, determined by area under the receiver operator characteristic (ROC) curve, was outstanding for stage 1 (Area under the ROC curve (AUC) = 0.93, 95% confidence interval (CI) = 0.89, 0.94), and excellent for stage 2 (AUC = 0.86, 95% CI = 0.83, 0.88). This is achieved based on an average of 5 items for stage 1 and an average of 6 items for stage 2, out of a bank of 73 previously validated items.

Original languageEnglish (US)
Pages (from-to)116-121
Number of pages6
JournalSchizophrenia Research
StatePublished - Jul 2022


  • Computerized adaptive diagnosis
  • Extremely randomized decision trees
  • Measurement
  • Psychosis

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry


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