Cited by 23Schizophrenia research and treatmentBipolar Disorder and TreatmentMental Health and Psychiatry

Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach

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Jauhar, S., Krishnadas, R., Nour, M.M., Cunningham-Owens, D., Johnstone, E.C., Lawrie, S.M.·2018·Schizophrenia Research

Abstract

Dubiety exists over whether clinical symptoms of schizophrenia can be distinguished from affective psychosis, the assumption being that absence of a "point of rarity" indicates lack of nosological distinction, based on prior group-level analyses. Advanced machine learning techniques, using unsupervised (hierarchical clustering) and supervised (regularized logistic regression algorithm and nested-cross-validation) were applied to a dataset of 202 patients with functional psychosis (schizophrenia n = 120, affective psychosis, n = 82). Patients were initially assessed with the Present State Examination (PSE), and followed up 2.5 years later, when DSM III diagnoses were applied (independent of initial PSE). Based on PSE syndromes, unsupervised learning discriminated depressive (approximately unbiased probability, AUP = 0.92) and mania/psychosis (AUP = 0.94) clusters. The mania/psychosis cluster further split into two groups - a mania (AUP = 0.84) and a psychosis cluster (AUP = 0.88). Supervised machine learning classified schizophrenia or affective psychosis with 83.66% (95% CI = 77.83% to 88.48%) accuracy. Area under the ROC curve (AUROC) was 89.14%. True positive rate for schizophrenia was 88.24% (95%CI = 81.05-93.42%) and affective psychosis 77.11% (95%CI = 66.58-85.62). Classification accuracy and AUROC remained high when PSE syndromes corresponding to affective symptoms (those that corresponded to the depressive and mania clusters) were removed. PSE syndromes, based on clinical symptoms, therefore discriminated between schizophrenia and affective psychosis, suggesting validity to these diagnostic constructs.

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Crossref · 10.1016/j.schres.2018.06.070

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  • There is also evidence for diagnostic specificity: machine learning reclassification studies demonstrated a distinction between schizophrenia and affective disorders; treatment requirements and outcomes also differ.

    “However, there is also evidence for diagnostic specificity: machine learning reclassification studies demonstrated a distinction between schizophrenia and affective disorders 213 ; treatment requirements and outcomes also differ 55”

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  1. Kraepelin, E., 1987. Dementia praecox. Cambridge University Press.
  2. Linscott, R.J., Allardyce, J., Van Os, J., 2010a. Seeking verisimilitude in a class: a systematic review of evidence that the criterial clinical symptoms of schizophrenia are taxonic. Schizophrenia bulletin 36, 811.

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Authors
Jauhar, S., Krishnadas, R., Nour, M. M., Cunningham-Owens, D., Johnstone, E. C., & Lawrie, S. M.
Affiliations
  • King's College London (GB)
  • Queen Elizabeth University Hospital (GB)
  • Royal Edinburgh Hospital (GB)
Corresponding author
Jauhar, S.
Venue
Schizophrenia Research
Publisher
Elsevier BV
Published
2018-12
Volume
202
Pages
241-247
ISSN
0920-9964 (ISSN-L)
OpenAlex
W2883616798
Cited by
23
References
37
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 4 · 2020: 8 · 2019: 4
FWCI
3.14
Type
journal article
Language
en
Open access
Locations
  • Schizophrenia Research (publishedVersion)
  • PubMed (publishedVersion)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • Research Portal (King's College London) (submittedVersion, cc-by-nc-nd)
Funders
  • NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
  • National Institute for Health and Care Research (ACF-2016-17-005)
  • King's College London
  • Menzies Centre for Australian Studies, King's College London, University of London
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
  • Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
  • Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
Keywords
schizophrenia, affective psychosis
MeSH terms
Machine Learning, Adolescent, Adult, Affective Disorders, Psychotic, Aged, Diagnosis, Computer-Assisted, Female, Humans, Male, Middle Aged, ROC Curve, Schizophrenia, Schizophrenic Psychology, Cohort Studies, Young Adult

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