In-session Predictors of Self-Harm Behavior in Dialectical Behavior Therapy

Purpose: Therapists are often charged with the seemingly impossible task of predicting their client’s future behavior, particularly behavior that may result in harm or death. Adverse events (AE) refer to a constellation of behaviors or events that interfere with treatment and exhibit a risk to the safety of the patient, which include suicide attempts, non-suicidal self injury (NSSI) and suicidal ideation. This is the first study that seeks to identify and associate in-session markers in DBT prior to AEs. Method: The proposed study sought to identify whether ruptures in therapeutic alliance (3RS; Eubanks-Carter, Muran & Safran, 2015), the frequency and intensity of negative-self referential speech (LIWC2015; Pennebaker, Booth, Boyd & Francis, 2015) and periods of psychomotor agitation are associated with AEs within a course of Dialectical Behavior Therapy (DBT). By coding videotaped psychotherapy sessions (n = 98) across 21 patients diagnosed with Borderline Personality Disorder (BPD), the researchers prospectively examined the association between in-session phenomena during the session prior to an AE. Exploratory logistic multilevel modeling, mean comparison and latent profile analysis (LPA) techniques were used to identified in-session markers associated with adverse events across the course of DBT treatment. Results: Using a multilevel model building approach to account for the nested structure, increases in content/affect split was associated with increased likelihood (36% increase in log-odds) of NSSI occurrence reported in the subsequent session when controlling for frequency of past NSSI episodes. When controlling for prior suicide attempts, withdrawal and confrontation ruptures did not predict the occurrence of suicide attempts in the subsequent session. To further examine the heterogeneity of the Level 1 variables (i.e., in-session markers), the LPA fitted afive-profile solution that captured relative differences in mean frequencies of coded markers.The latent “session types” were named based on their in-session characteristics, with AEs identified post-hoc within the identified profiles. While AEs were distributed across multiple profiles, visual inspection aligned with the findings in the multilevel model. Sessions characterized by elevations in content/affect split and behaviors that distance from the therapist preceded NSSI during treatment. The majority of the sessions prior to suicide attempts (70%) during the study period were assigned to the profile with the lowest mean frequency of in-session markers. Clinical implications: The strength of the therapeutic alliance in DBT is an essential component of effective treatment. Therapeutic ruptures, particularly withdrawal ruptures, occur frequently in DBT treatment. Attending to these ruptures, especially occasions when a patient’s affect and verbal content are not congruent, may signal to the therapist that the patient requires additional support. In-session content/affect split may represent a vulnerability factor that puts the patient at increased risk of NSSI behavior due difficulty attuning to their internal experiences and limitations in their emotional flexibility. Limitations: Similar to other studies that examine self-harm, the low base-rate of suicide attempts and NSSI behavior complicates empirical study. Since the study utilized strict inclusion criteria for only individuals diagnosed with BPD, findings cannot be generalized to patients with other psychiatric diagnoses. While some therapist effects are controlled for in the study since one therapist treated all the patient included in the study, the study does not account for therapist factors that may influence the therapy dyad. Given the limited sample size, there was not adequate power to fit more complicated models (e.g., inter-level and intra-level interactions, random effect predictor variables, etc.)

Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a widely accepted means of diagnosis, but patients are often embarrassed to get diagnosed at clinics. The speech signal based system is a recently developed alternative. Unfortunately,PTSD speech corpora are limited in size which presents difficulties in training complex diagnostic models. This dissertation proposed sparse coding methods and deep belief network models for emotional state identification and PTSD diagnosis. It also includes an additional transfer learning strategy for PTSD diagnosis. Deep belief networks are complex models that cannot work with small data like the PTSD speech database. Thus, a transfer learning strategy was adopted to mitigate the small data problem. Transfer learning aims to extract knowledge from one or more source tasks and apply the knowledge to a target task with the intention of improving the learning. It has proved to be useful when the target task has limited high quality training data. We evaluated the proposed methods on the speech under simulated and actual stress database (SUSAS) for emotional state recognition and on two PTSD speech databases for PTSD diagnosis. Experimental results and statistical tests showed that the proposed models outperformed most state-of-the-art methods in the literature and are potentially efficient models for emotional state recognition and PTSD diagnosis

Staging evaluation of posttraumatic stress disorder : a machine learning study

Os transtornos de estresse relacionados a um evento traumático, como o transtorno de estresse agudo (TEA) e o transtorno de estresse pós-traumático (TEPT), são caracterizados por alta morbidade e prejuízo social significativo. No Brasil, estima-se que 80% da população já foi exposta a pelo menos um evento traumático ao longo da vida em grandes centros urbanos, como São Paulo e Rio de Janeiro; o crescente problema da violência urbana mostra-se fator importante para a gênese dos transtornos relacionados ao trauma. Devido à etiologia do TEPT ser multicausal e complexa, técnicas de Machine Learning (Aprendizado de Máquina – ML) tem sido usadas para desenvolver escores de risco, para predição diagnóstica e para definição de tratamento. Contudo, considerando sua heterogeneidade clínica e etiológica, realizar o diagnóstico e definir um tratamento adequado pode ser muitas vezes desafiador. O uso do estadiamento clínico surge como um método mais refinado de diagnóstico, procurando definir a progressão do transtorno em momentos específicos durante o continuum da enfermidade. Esta abordagem pode auxiliar em um diagnóstico mais aprimorado, conhecer melhor o prognóstico e escolher o melhor tratamento de acordo com o estágio do transtorno. Assim, o TEPT aparece como um exemplo importante de como um método de estadiamento pode trazer benefícios. O objetivo desta tese é avaliar como os aspectos pessoais, clínicos e relacionados ao trauma dos pacientes atendidos em ambulatórios especializados em trauma psíquico podem estar relacionados à predição do estadiamento clínico de TEPT usando técnicas de ML.Stress disorders related to a traumatic event, such as acute stress disorder (ASD) and posttraumatic stress disorder (PTSD), are characterized by high morbidity and significant social impairment. In Brazil, it is estimated that 80% of the population has already been exposed to at least one traumatic event throughout life in large urban centers, such as São Paulo and Rio de Janeiro; the growing problem of urban violence proves to be an important factor in the genesis of trauma-related disorders. The etiology of PTSD is multicausal and complex; techniques of Machine Learning (ML) have been used to develop PTSD risk scores, to predict its diagnosis and to choose better treatments. However, considering its clinical and etiological heterogeneity, making the diagnosis and defining an appropriate treatment can often be challenging. The use of clinical staging appears as a refined method of diagnosis, aiming to define the progression of the disorder at specific times during the continuum of the illness. This approach may provide improved diagnosis, better understand the prognosis and choose the best treatment according to the stage of the disorder. Thus, PTSD appears as an important example of how a staging method can bring benefits. The objective of this thesis is to evaluate how the personal, clinical and trauma-related aspects of patients who sought care at outpatient clinics specialized in emotional trauma can be related to the prediction of the PTSD staging using ML techniques

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp