Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach
Open accessAbstract
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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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”
From Preventive psychiatry: a blueprint for improving the mental health of young people (2021) · cites this paper as [213]
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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
- green
- eprints.gla.ac.uk
- Licence
- 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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