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Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Ecg Recordings As Predictors Of Very Early Autism Likelihood: A Machine Learning Approach, Deepa Tilwani, Jessica Bradshaw, Amit Sheth, Christian O'Reilly

Publications

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker …

Tutorial - Shodhguru Labs: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Issue Of Data Imbalance On Low Birthweight Baby Outcomes Prediction And Associated Risk Factors Identification: Establishment Of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies, Yang Ren, Dezhi Wu, Yan Tong, Ana Lopez-De Fede

Publications

Background: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models’ actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML …

Association Of Hospital Incentive Care Management Partnerships For Uninsured Patients With Emergency Department Utilization, Sarah Gareau, Ana Lopez-De Fede, Zhimin Chen, Nathaniel Bell

Publications

IMPORTANCE The South Carolina (SC) Healthy Outcomes Plan (HOP) program aimed to expand access to health care to individuals without insurance; it remains unknown whether there is an association between the SC HOP program and emergency department (ED) use among patients with high health care costs and needs. OBJECTIVES To determine whether participation in the SC HOP was associated with reduced ED utilization among uninsured participants. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included 11 684 HOP participants (ages 18-64 years) with at least 18 months of continuous enrollment. Generalized estimating equations and segmented regression of interrupted time-series analyses …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Defining And Detecting Toxicity On Social Media: Context And Knowledge Are Key, Amit Sheth, Valerie Shalin, Ugur Kursuncu

Publications

As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper We define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge …

Cyber Social Threats 2021: Ai, Covid-19 Vaccine, Detection And Countering Strategies, Ugur Kursuncu, Jeremy Blackburn, Yelena Mejova, Megan Squire, Amit Sheth

Publications

In recent years, online platforms have been utilized for promoting harmful content and behavior such as extremism, harassment, mis/disinformation, human trafficking, genderbased violence among others affecting our society, often leading to real-world events. Such content and behaviors are inherently complex, making the recognition of these narratives challenging for researchers as well as social media companies. The Cyber Social Threats (CySoc) Workshop 2021 aimed to facilitate a rich forum for researchers and practitioners from both academia and industry in the areas of computing and social science, to discuss novel avenues for research on interdisciplinary aspects of harmful communications on social media, …

Owsnet: Towards Real-Time Offensive Words Spotting Network For Consumer Iot Devices, Bharath Sudharsan, Sweta Malik, Peter Corcoran, Pankesh Patel, John G. Breslin, Muhammad Intizar Ali

Publications

Every modern household owns at least a dozen of IoT devices like smart speakers, video doorbells, smartwatches, where most of them are equipped with a Keyword spotting(KWS) system-based digital voice assistant like Alexa. The state-of-the-art KWS systems require a large number of operations, higher computation, memory resources to show top performance. In this paper, in contrast to existing resource-demanding KWS systems, we propose a light-weight temporal convolution based KWS system named OWSNet, that can comfortably execute on a variety of IoT devices around us and can accurately spot multiple keywords in real-time without disturbing the device's routine functionalities.

When OWSNet …

Knowledge Infused Policy Gradients With Upper Confidence Bound For Relational Bandits, Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

Publications

Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc. However, most of the algorithms use at feature vectors to represent context whereas, in the real world, there is a varying number of objects and relations among them to model in the context. For example, in a music recommendation system, the user context contains what music they listen to, which artists create this music, the artist albums, etc. Adding richer relational context representations also introduces a much larger context space making exploration-exploitation harder. To improve the efficiency of exploration-exploitation knowledge about the …

Identifying Depressive Symptoms From Tweets: Figurative Language Enabled Multitask Learning Framework, Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit P. Sheth, Jeremiah Schumm

Publications

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the …

Cyber Social Threats 2020 Workshop Meta-Report: Covid-19, Challenges, Methodological And Ethical Considerations, Ugur Kursuncu, Yelena Mejova, Jeremy Blackburn, Amit Sheth

Publications

Online platforms have been increasingly misused by ill-intentioned actors, affecting our society, often leading to real-world events of social significance. On the other hand, recog-nizing the narratives related to harmful behaviors is challeng-ing due to its complex and sensitive nature. The Cyber SocialThreats Workshop 2020 aimed to stimulate research for thechallenges on methodological and ethical considerations indeveloping novel approaches to analyze online harmful con-versations, concerning social, cultural, emotional, commu-nicative, and linguistic aspects. It provided a forum to bringtogether researchers and practitioners from both academiaand industry in the areas of computational social sciences, so-cial network analysis and mining, natural language process-ing, …

Multimodal Mental Health Analysis In Social Media, Amir Hossein Yazdavar, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amit P. Sheth, Amir Hassan Monadjemi, Krishnaprasad Thirunarayan, John M. Meddar, Annie Myers, Jyotishman Pathak, Pascal Hitzler

Publications

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive …

Alone: A Dataset For Toxic Behavior Among Adolescents On Twitter, Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth, I. Budak Arpinar

Publications

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive …

Determination Of Personalized Asthma Triggers From Multimodal Sensing And A Mobile App: Observational Study, Revathy Venkataramanan, Krishnaprasad Thirunarayan, Utkarshani Jaimini, Dipesh Kadariya, Hong Yung Yip, Maninder Kalra, Amit Sheth

Publications

Background: Asthma is a chronic pulmonary disease with multiple triggers. It can be managed by strict adherence to an asthma care plan and by avoiding these triggers. Clinicians cannot continuously monitor their patients’ environment and their adherence to an asthma care plan, which poses a significant challenge for asthma management.

Objective: In this study, pediatric patients were continuously monitored using low-cost sensors to collect asthma-relevant information. The objective of this study was to assess whether kHealth kit, which contains low-cost sensors, can identify personalized triggers and provide actionable insights to clinicians for the development of a tailored asthma care plan. …

Road Accidents Bigdata Mining And Visualization Using Support Vector Machines, Usha Lokala, Srinivas Nowduri, Prabhakar K Sharma

Publications

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new …

Ring Maps For Spatial Visualization Of Multivariate Epidemiological Data, Sarah E. Battersby, John E. Stewart, Ana Lopez-De Fede, Kevin C. Remington

Publications

Epidemiological research often involves the visual exploration of numerous attributes to help discern patterns between health and characteristics of the physical, socioeconomic, or built environment. Unfortunately, many of the multivariate mapping techniques discussed throughout the cartographic literature can be challenging to create or interpret—particularly for individuals without a cartographic background. In this paper, we present a new style of multivariate map—the ring map—to aid in basic visualization of multivariate datasets. For purposes of example, we focus on the use of the ring map style for exploring county-level epidemiological data for the state of South Carolina to examine patterns of age, …