Transitioning to a Data Driven Mental Health Practice: Collaborative Expert Sessions for Knowledge and Hypothesis Finding

The surge in the amount of available data in health care enables a novel, exploratory research approach that revolves around finding new knowledge and unexpected hypotheses from data instead of carrying out well-defined data analysis tasks. We propose a specification of the Cross Industry Standard Process for Data Mining (CRISP-DM), suitable for conducting expert sessions that focus on finding new knowledge and hypotheses in collaboration with local workforce. Our proposed specification that we name CRISP-IDM is evaluated in a case study at the psychiatry department of the University Medical Center Utrecht. Expert interviews were conducted to identify seven research themes in the psychiatry department, which were researched in cooperation with local health care professionals using data visualization as a modeling tool. During 19 expert sessions, two results that were directly implemented and 29 hypotheses for further research were found, of which 24 were not imagined during the initial expert interviews. Our work demonstrates the viability and benefits of involving work floor people in the analyses and the possibility to effectively find new knowledge and hypotheses using our CRISP-IDM method

Psychosis as an Evolutionary Adaptive Mechanism to Changing Environments

Background: From an evolutionary perspective it is remarkable that psychotic disorders, mostly occurring during fertile age and decreasing fecundity, maintain in the human population. Aim: To argue the hypothesis that psychotic symptoms may not be viewed as an illness but as an adaptation phenomenon, which can become out of control due to different underlying brain vulnerabilities and external stressors, leading to social exclusion. Methods: A literature study and analysis. Results: Until now, biomedical research has not unravelld the definitive etiology of psychotic disorders. Findings are inconsistent and show non-specific brain anomalies and genetic variation with small effect sizes. However, compelling evidence was found for a relation between psychosis and stressful environmental factors, particularly those influencing social interaction. Psychotic symptoms may be explained as a natural defense mechanism or protective response to stressful environments. This is in line with the fact that psychotic symptoms most often develop during adolescence. In this phase of life, leaving the familiar, and safe home environment and building new social networks is one of the main tasks. This could cause symptoms of “hyperconsciousness” and calls on the capacity for social adaptation. Conclusions: Psychotic symptoms may be considered as an evolutionary maintained phenomenon.Research investigating psychotic disorders may benefit from a focus on underlying general brain vulnerabilities or prevention of social exclusion, instead of psychotic symptoms

The role of client empathy in treatment outcome in a sample of adolescents referred to forensic youth psychiatric services

Starting from the assumption that empathy is crucial in the therapeutic process, the current study explored whether client empathy before treatment relates to treatment outcome, whether client empathy is subject to change in the first six months of treatment, whether such change relates to treatment outcome and whether therapist factors relate to possible changes in client empathy. In total 90 adolescents treated by 31 therapists at forensic psychiatric services participated in the study. Client empathy was assessed with self-report questionnaires of affective and cognitive empathy at intake and again at six months of treatment. Therapeutic change was rated by their therapist. Client empathy before treatment was not systematically related to treatment outcome. Cognitive empathy tended to improve during treatment, stronger in girls than boys, and depending in part on the therapist's gender: Under conditions of a male (not female) therapist boys reported less improvement in cognitive empathy than girls. The most consistent study result was that improvement in cognitive empathy contributed positively to treatment outcome. The study provides new data on the role of client empathy in the treatment of forensic youth psychiatric patients. If replicated, these findings have important implications for treatment and training in juvenile forensic psychiatry

Psychosis as an evolutionary adaptive mechanism to changing environments

__Background:__ From an evolutionary perspective it is remarkable that psychotic disorders, mostly occurring during fertile age and decreasing fecundity, maintain in the human population. __Aim:__ To argue the hypothesis that psychotic symptoms may not be viewed as an illness but as an adaptation phenomenon, which can become out of control due to different underlying brain vulnerabilities and external stressors, leading to social exclusion. __Methods:__ A literature study and analysis. __Results:__ Until now, biomedical research has not unravelld the definitive etiology of psychotic disorders. Findings are inconsistent and show non-specific brain anomalies and genetic variation with small effect sizes. However, compelling evidence was found for a relation between psychosis and stressful environmental factors, particularly those influencing social interaction. Psychotic symptoms may be explained as a natural defense mechanism or protective response to stressful environments. This is in line with the fact that psychotic symptoms most often develop during adolescence. In this phase of life, leaving the familiar, and safe home environment and building new social networks is one of the main tasks. This could cause symptoms of "hyperconsciousness" and calls on the capacity for social adaptation. __Conclusions:__ Psychotic symptoms may be considered as an evolutionary maintained phenomenon. Research investigating psychotic disorders may benefit from a focus on underlying general brain vulnerabilities or prevention of social exclusion, instead of psychotic symptoms

Neuroanatomical abnormalities in first-episode psychosis across independent samples: a multi-centre mega-analysis

Abstract Background Neuroanatomical abnormalities in first-episode psychosis (FEP) tend to be subtle and widespread. The vast majority of previous studies have used small samples, and therefore may have been underpowered. In addition, most studies have examined participants at a single research site, and therefore the results may be specific to the local sample investigated. Consequently, the findings reported in the existing literature are highly heterogeneous. This study aimed to overcome these issues by testing for neuroanatomical abnormalities in individuals with FEP that are expressed consistently across several independent samples. Methods Structural Magnetic Resonance Imaging data were acquired from a total of 572 FEP and 502 age and gender comparable healthy controls at five sites. Voxel-based morphometry was used to investigate differences in grey matter volume (GMV) between the two groups. Statistical inferences were made at p < 0.05 after family-wise error correction for multiple comparisons. Results FEP showed a widespread pattern of decreased GMV in fronto-temporal, insular and occipital regions bilaterally; these decreases were not dependent on anti-psychotic medication. The region with the most pronounced decrease – gyrus rectus – was negatively correlated with the severity of positive and negative symptoms. Conclusions This study identified a consistent pattern of fronto-temporal, insular and occipital abnormalities in five independent FEP samples; furthermore, the extent of these alterations is dependent on the severity of symptoms and duration of illness. This provides evidence for reliable neuroanatomical alternations in FEP, expressed above and beyond site-related differences in anti-psychotic medication, scanning parameters and recruitment criteria

Using machine learning and structural neuroimaging to detect first episode psychosis:reconsidering the evidence

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation

Clinical characteristics of women captured by extending the definition of severe postpartum haemorrhage with 'refractoriness to treatment': a cohort study

Background: The absence of a uniform and clinically relevant definition of severe postpartum haemorrhage hampers comparative studies and optimization of clinical management. The concept of persistent postpartum haemorrhage, based on refractoriness to initial first-line treatment, was proposed as an alternative to common definitions that are either based on estimations of blood loss or transfused units of packed red blood cells (RBC). We compared characteristics and outcomes of women with severe postpartum haemorrhage captured by these three types of definitions. Methods: In this large retrospective cohort study in 61 hospitals in the Netherlands we included 1391 consecutive women with postpartum haemorrhage who received either ≥4 units of RBC or a multicomponent transfusion. Clinical characteristics and outcomes of women with severe postpartum haemorrhage defined as persistent postpartum haemorrhage were compared to definitions based on estimated blood loss or transfused units of RBC within 24 h following birth. Adverse maternal outcome was a composite of maternal mortality, hysterectomy, arterial embolisation and intensive care unit admission. Results: One thousand two hundred sixty out of 1391 women (90.6%) with postpartum haemorrhage fulfilled the definition of persistent postpartum haemorrhage. The majority, 820/1260 (65.1%), fulfilled this definition within 1 h following birth, compared to 819/1391 (58.7%) applying the definition of ≥1 L blood loss and 37/845 (4.4%) applying the definition of ≥4 units of RBC. The definition persistent postpartum haemorrhage captured 430/471 adverse maternal outcomes (91.3%), compared to 471/471 (100%) for ≥1 L blood loss and 383/471 (81.3%) for ≥4 units of RBC. Persistent postpartum haemorrhage did not capture all adverse outcomes because of missing data on timing of initial, first-line treatment. Conclusion: The definition persistent postpartum haemo

Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text

Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results have been obtained using classical techniques, such as bag-of-words features, in combination with statistical models. Recently however Deep Learning techniques, such asWord Embeddings and Recurrent Neural Networks, have shown to possibly have even greater potential. In this work, we apply several Deep Learning and classical machine learning techniques to the task of predicting violence incidents during psychiatric admission using clinical text that is already registered at the start of admission. For this purpose, we use a novel and previously unexplored dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. Results show that predicting violence incidents with state-of-the-art performance is possible, and that using Deep Learning techniques provides a relatively small but consistent improvement in performance. We finally discuss the potential implication of our findings for the psychiatric practice

Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text

Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results have been obtained using classical techniques, such as bag-of-words features, in combination with statistical models. Recently however Deep Learning techniques, such asWord Embeddings and Recurrent Neural Networks, have shown to possibly have even greater potential. In this work, we apply several Deep Learning and classical machine learning techniques to the task of predicting violence incidents during psychiatric admission using clinical text that is already registered at the start of admission. For this purpose, we use a novel and previously unexplored dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. Results show that predicting violence incidents with state-of-the-art performance is possible, and that using Deep Learning techniques provides a relatively small but consistent improvement in performance. We finally discuss the potential implication of our findings for the psychiatric practice

Gaming the system: building an online management game to spread and gather insights into the dynamics of performance management systems

Extensive research has produced many insights into the dynamics of performance management systems. Spreading these complex insights among students and practitioners can be a daunting task. Gathering new insights can be equally challenging. This article introduces a novel tool for teaching and researching performance management, reporting on the design and first use of a free online management game. Players take the role of a hospital manager trying to satisfy multiple stakeholders through applying different performance management instruments. While students learn about the complexities of performance management, researchers gather data about the pathways individuals pursue while navigating performance management systems