Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec.

BACKGROUND: Recent initiatives in psychiatry emphasize the utility of characterizing psychiatric symptoms in a multidimensional manner. However, strategies for applying standard self-report scales for multiaxial assessment have not been well-studied, particularly where the aim is to support both categorical and dimensional phenotypes. METHODS: We propose a method for applying natural language processing to derive dimensional measures of psychiatric symptoms from questionnaire data. We utilized nine self-report symptom measures drawn from a large cellular biobanking study that enrolled individuals with mood and psychotic disorders, as well as healthy controls. To summarize questionnaire results we used word embeddings, a technique to represent words as numeric vectors preserving semantic and syntactic meaning. A low-dimensional approximation to the embedding space was used to derive the proposed succinct summary of symptom profiles. To validate our embedding-based disease profiles, these were compared to presence or absence of axis I diagnoses derived from structured clinical interview, and to objective neurocognitive testing. RESULTS: Unsupervised and supervised classification to distinguish presence/absence of axis I disorders using survey-level embeddings remained discriminative, with area under the receiver operating characteristic curve up to 0.85, 95% confidence interval (CI) (0.74,0.91) using Gaussian mixture modeling, and cross-validated area under the receiver operating characteristic curve 0.91, 95% CI (0.88,0.94) using logistic regression. Derived symptom measures and estimated Research Domain Criteria scores also associated significantly with performance on neurocognitive tests. CONCLUSIONS: Our results support the potential utility of deriving dimensional phenotypic measures in psychiatric illness through the use of word embeddings, while illustrating the challenges in identifying truly orthogonal dimensions.

High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.

BACKGROUND: Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes. METHODS: We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative clinical notes. Estimates of Research Domain Criteria loading were derived from a cohort of 3619 individuals with 4623 hospital admissions. We applied this tool to a large corpus of psychiatric inpatient admission and discharge notes (2010-2015), and using the same cohort we examined face validity, predictive validity, and convergent validity with gold standard annotations. RESULTS: In mixed-effect models adjusted for sociodemographic and clinical features, greater negative and positive symptom domains were associated with a shorter length of stay (ß = -.88, p = .001 and ß = -1.22, p < .001, respectively), while greater social and arousal domain scores were associated with a longer length of stay (ß = .93, p < .001 and ß = .81, p = .007, respectively). In fully adjusted Cox regression models, a greater positive domain score at discharge was also associated with a significant increase in readmission risk (hazard ratio = 1.22, p < .001). Positive and negative valence domains were correlated with expert annotation (by analysis of variance [df = 3], R2 = .13 and .19, respectively). Likewise, in a subset of patients, neurocognitive testing was correlated with cognitive performance scores (p < .008 for three of six measures). CONCLUSIONS: This shows that natural language processing can be used to efficiently and transparently score clinical notes in terms of cognitive and psychopathologic domains.

Lessons from social network analyses for behavioral medicine.

PURPOSE OF REVIEW: This study presents an overview of the rapidly expanding field of social network analysis, with an emphasis placed on work relevant to behavioral health clinicians and researchers. I outline how social network analysis is a distinct empirical methodology within the social sciences that has the potential to deepen our understanding of how mental health and addiction are influenced by social environmental factors. RECENT FINDINGS: Whereas there have been a number of recent studies in the mental health literature that discuss social influences on mental illness and addiction, and a number of studies looking at how social networks influence health and behaviors, there are still relatively few studies that combine the two. Those that have suggest that mood symptoms as well as alcohol consumption are clustered within, and may travel along, social networks. SUMMARY: Social networks appear to have an important influence on a variety of mental health conditions. This avenue of research has the potential to influence both clinical practice and public policy.