Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data.

Background: Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Background: Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them.

Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness.

Background: Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness.

Lexical stability of psychiatric clinical notes from electronic health records over a decade.

Objective: Natural language processing (NLP) methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice - as well as the systems and databases in which clinical notes are recorded and stored - change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models.

Stability of diagnostic coding of psychiatric outpatient visits across the transition from the second to the third version of the Danish National Patient Registry.

Objective: In Denmark, data on hospital contacts are reported to the Danish National Patient Registry (DNPR). The ICD-10 main diagnoses from the DNPR are often used as proxies for mental disorders in psychiatric research. With the transition from the second version of the DNPR (DNPR2) to the third (DNPR3) in February-March 2019, the way main diagnoses are coded in relation to outpatient treatment changed substantially.

Men with high serotonin 1B receptor binding respond to provocations with heightened amygdala reactivity.

Serotonin signalling influences amygdala reactivity to threat-related emotional facial expressions in healthy adults, but in vivo serotonin signalling has never been investigated in the context of provocative stimuli in aggressive individuals. The aim of this study was to evaluate associations between serotonin 1B receptor (5-HTR) levels and brain reactivity to provocations. We quantified regional 5-HTR binding using [C]AZ10419369 positron emission tomography (PET) and measured brain activation following provocations with functional magnetic resonance imaging (fMRI) in eighteen violent offenders and 25 healthy control subjects.

Testosterone levels in healthy men correlate negatively with serotonin 4 receptor binding.

The serotonergic system integrates sex steroid information and plays a central role in mood and stress regulation, cognition, appetite and sleep. This interplay may be critical for likelihood of developing depressive episodes, at least in a subgroup of sensitive individuals. The serotonin 4 receptor (5-HTR) indexes central serotonergic tonus, which may be related to endogenous sex-steroid levels in the mentally healthy state even though this remains elusive.

Violent offenders respond to provocations with high amygdala and striatal reactivity.

The ability to successfully suppress impulses and angry affect is fundamental to control aggressive reactions following provocations. The aim of this study was to examine neural responses to provocations and aggression using a laboratory model of reactive aggression. We used a novel functional magnetic resonance imaging point-subtraction aggression paradigm in 44 men, of whom 18 were incarcerated violent offenders and 26 were control non-offenders.

Serotonin 1B Receptor Binding Is Associated With Trait Anger and Level of Psychopathy in Violent Offenders.

Background: The involvement of serotonin in aggression has traditionally been attributed to impaired prefrontal serotonergic inhibitory control of emotional reactions to provocations in antisocial individuals. However, it is unclear which specific serotonergic receptors are involved in the effects. A large body of preclinical research supports a specific role of serotonin 1B receptors (5-HTRs) in aggression and impulsivity, but this has never been evaluated in humans.