Detecting risk for treatment nonresponse among families of young children with behavior problems: Candidate tailoring variables and early decision points for adaptive interventions

Heterogeneity in mental health treatment outcomes and high rates of treatment nonresponse highlight the need for adaptive interventions that align with precision mental health care approaches to tailor treatments according to individual differences in progress over time. Modern clinical trial methodologies and analytic strategies can inform dynamic mental health treatment decisions, but the potential to improve patient outcomes is only as strong as the extent to which selected tailoring variables (i.e., interim response factors that dictate whether treatment should shift course) accurately detect risk for treatment nonresponse. Identifying empirically informed tailoring variables and the most appropriate timepoint(s) to assess them (i.e., critical decision points) is essential in order to design adaptive interventions. This dissertation is comprised of three manuscripts focused on the use of early interim progress data to detect risk for mental health treatment nonresponse. First, I detail a strategy that leverages secondary data analysis to examine candidate tailoring variables at candidate critical decision points, and their relationships with treatment nonresponse. Then, I directly apply this strategy to a pooled sample of families who presented for treatment of early childhood behavior problems (N=153). This study showed that using dichotomous classifications of early interim treatment progress yielded limited utility in differentially predicting post-treatment response when examined in isolation from one another. Thus, I subsequently adopt a continuous approach to measuring early interim treatment progress and examine whether interactions between early indicators of treatment response predict symptom trajectories in a sample of families who participated in a behavioral parenting intervention (BPI) for early childhood developmental delay and behavior problems (N=70). Findings from the third paper suggest symptom response trajectories can be predicted by examining the interaction between caregiver skills and child behavior problems displayed within the first six sessions of a BPI. Collectively, this collection of work encourages the use of routine outcome monitoring to assess multiple domains of early interim treatment progress. To improve the efficiency and effectiveness of mental health care, future work should continue to use analytic approaches that capture the dynamic interplay among multiple early interim response factors that can optimally inform clinical decision-making practices throughout treatment

Graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors, A

2017 Summer.Includes bibliographical references.The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms

Feasibility of Twitter sentiment analysis in predicting crime in the UAE

In this study, we demonstrate how the information provided by individuals on social media can represent some aspects of their behavior to predict criminal activities and intentions in societies. The problem discussed in this study is finding a model that can analyze social media posts to infer the intentions and feelings of the publisher behind those posts to predict the probability of committing a crime. This is a preventive technique that can be used to monitor individuals or organizations who have a behavioral pattern that can be inferred as criminal intent. To help detect and predict criminal activities, we observe the use of data mining followed by sentiment analysis on Twitter. This well-known online social network enables users to post small texts, aka tweets, that are up to 280 characters in length each. In the U.S. was the main That of the study and data collection. First, the targeted tweets were collected according to geographical and keyword-based filters. Then a sentiment analysis was applied to analyze the crime intensity in specific locations. A correlation was found between the collected tweets related to criminal activities and the crime rates in the corresponding cities. Furthermore, another analysis study that we based in the United Arab Emirates found out that the quality of tweets is lower than the tweets in the United States due to the number of spam tweets. The lack of coloration between tweets and crimes committed on the ground due to laws prohibiting sharing information of crimes from police departments

From the womb into the world:Protecting the fetal brain from maternal stress during pregnancy

No other period in a child's life matches the speed of brain development than the first nine months in the womb. Rapid growth goes hand in hand with enormous potential, but also with great vulnerability. This policy-focused review focuses on maternal mental health as a key factor for fetal brain development. Already during pregnancy, the fetal brain wires differently when exposed to maternal stress, and children prenatally exposed to stress have a higher risk of developing neurodevelopmental disorders. Maternal prenatal stress is preventable, treatable, and tractable by policy. Research-based, policy recommends: (1) screening for maternal mental health issues throughout pregnancy, (2) encourage talking about prenatal mental health, (3) evidence-based interventions for pregnant women with mental health issues, (4) avoiding stress-inducing communication towards pregnant women, and (5) stimulating positive postnatal parenting. Investing in healthy pregnancies will improve fetal brain growth, and, ultimately lead to a healthier next generation