Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients

Abstract

Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3 days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such as age, gender, history of alcohol use, violent behaviors, etc., and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation.

A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry

“…They used deep learning methods, achieving micro-averaged F-measures in 60’s for eleven common mental health conditions. Dai et al [16] studied the relationship between violence and “clinical and social parameters” finding that important determinants of violent behavior included family “history of violent behavior”, suicidal tendencies, “presence of financial stress”, and most importantly, use of stimulants.…”

“…These data could be used for mental health-related research questions that go beyond what is posed by the challenge organizers. The novel data use track was for participants who wanted to utilize the RDoC data to address new research questions, e.g., [14–16]. …”

Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

“…This dataset has been used to predict the mental health condition of a patient [ 19 ]. In an another study, this dataset is used to analyse associations between clinical/social parameters and violent behaviour in patients with psychiatric disorders [ 20 ]. Please refer to S1 and S2 Figs and the work represented by Uzuner, Stubbs [ 5 ] for the demographic information and details on the manual annotations of the dataset.…”

RDoC Task at BioNLP-OST 2019

“…CLPsych 2016 Shared Task [18]) revolved around classifying the severity of peer support forum posts. One of the i2b2 7 challenges from 2011 focused on the sentiment analysis of suicide notes [22,21].…”

“…In 2017, Uzuner et al introduced the "The RDoC for Psychiatry" challenge, which was composed of three tracks: de-identification of mental health records [28], determination of symptom severity from a psychiatric evaluation of a patient) related to one of the RDoC domains) [9], and the use of mental health records released through the challenge for answering novel questions [32,29,7]. In contrast, the RDoC task is a combination of information retrieval and sentence extraction from Biomedical literature related to RDoC constructs.…”