Development of a computerized adaptive diagnostic screening tool for psychosis

Schizophr Res. 2022 Jul:245:116-121. doi: 10.1016/j.schres.2021.03.020. Epub 2021 Apr 6.


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.

Keywords: Computerized adaptive diagnosis; Extremely randomized decision trees; Measurement; Psychosis.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bipolar Disorder* / diagnosis
  • Bipolar Disorder* / psychology
  • Humans
  • Machine Learning
  • Outpatients
  • Psychotic Disorders* / diagnosis
  • Schizophrenia* / diagnosis