Abstract
BackgroundA high risk for suicidal behaviour has long been recognized in psychotic disorders. More recently, research has demonstrated that subclinical psychotic experiences are strong markers of risk for suicidal behaviour. Whether PEs are specific risk markers for suicidal behaviour, beyond the indirect risk resulting from co-occurring psychopathology, remains unclear.MethodsThis study used a stratified, multi-stage probability sample of households in England to recruit a nationally representative sample aged 16 years and over (N=7,403). Participants were assessed for psychotic experiences, suicide attempts, common mental disorders and borderline personality disorder/traits.ResultsPsychotic experiences were reported by approximately 4% (n=323) of the total sample and were prevalent across the full range of mental disorders: the highest prevalence in non-psychotic disorders was in individuals with agoraphobia, nearly a quarter of whom reported psychotic experiences. Eighteen percent of individuals with social phobia reported hallucinations, as did 17% of individuals with OCD, 14% of individuals with depression, and 11% of individuals with generalised anxiety disorder. Psychotic experiences were risk markers for suicide attempts, regardless of whether they occurred in individuals with a common mental disorder (OR=2.47, 95%CI=1.37–4.43), individuals without a common mental disorder (OR=3.99, 95%CI=2.47–6.43), individuals with high borderline personality disorder traits (OR=2.23, 95%CI=1.03–4.85) or individuals without significant borderline personality disorder traits (OR=2.47, 95%CI=1.37–4.43).DiscussionPsychotic experiences are prevalent across a wide range of (non-psychotic) mental disorders. They demonstrate a strong relationship with suicidal behaviour, beyond that explained by co-occurring mental disorder diagnoses.
Highlights
The internet and social media provide an unprecedented opportunity to transform early psychosis intervention services
Preliminary classifiers correctly recognized participants with psychotic disorders (n=62) from healthy controls (n=24) with an average accuracy of 80% and distinguished participants with psychosis from those with mood disorders (n=39) with an average accuracy of 70%
We identified significant differences in the profile pictures (p
Summary
Diagnosis of schizophrenia is based on a collection of symptoms which are heterogeneous from one patient to the other. The aim of the current study is to assess the predictability of schizophrenia diagnosis applying machine learning techniques to an ensemble of genetic, early environmental and cognitive deficits variables. Data from Modalities 1, 2 and 3 entered NeuroMiner v0.998 and underwent preprocessing procedures through scaling, pruning of non-informative variables and imputation of missing values through Euclidean distance-based nearest-neighbor search. These three modalities were included in a Support Vector Machine HC vs SCZ classification algorithm, which applied decision-based data fusion strategies to integrate the individual predictions of the three modalities in a nested cross-validation framework. Decision-based fusion combining individual cognitive, environmental and genetic decision scores predicted the classification of SCZ from HC with a 78.9% BAC
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