Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Principles for Designing Context-Aware Applications for Physical Activity Promotion

Mobile devices with embedded sensors have become commonplace, carried by billions of people worldwide. Their potential to influence positive health behaviors such as physical activity in people is just starting to be realized. Two critical ingredients, an accurate understanding of human behavior and use of that knowledge for building computational models, underpin all emerging behavior change applications. Early research prototypes suggest that such applications would facilitate people to make difficult decisions to manage their complex behaviors. However, the progress towards building real-world systems that support behavior change has been much slower than expected. The extreme diversity in real-world contextual conditions and user characteristics has prevented the conception of systems that scale and support end-users’ goals. We believe that solutions to the many challenges of designing context-aware systems for behavior change exist in three areas: building behavior models amenable to computational reasoning, designing better tools to improve our understanding of human behavior, and developing new applications that scale existing ways of achieving behavior change. With physical activity as its focus, this thesis addresses some crucial challenges that can move the field forward. Specifically, this thesis provides the notion of sweet spots, a phenomenological account of how people make and execute their physical activity plans. The key contribution of this concept is in its potential to improve the predictability of computational models supporting physical activity planning. To further improve our understanding of the dynamic nature of human behavior, we designed and built Heed, a low-cost, distributed and situated self-reporting device. Heed’s single-purpose and situated nature proved its use as the preferred device for self-reporting in many contexts. We finally present a crowdsourcing system that leverages expert knowledge to write personalized behavior change messages for large-scale context-aware applications.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144089/1/gparuthi_1.pd

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

On the relation between body and movement space representation: an experimental investigation on spinal cord injured people

Body Representation (BR) and Movement Space Perception (MSP) are fundamental for human beings in order to move in space and interact with object s and other people. Both BR and space representation change after spinal cord injuries in complete paraplegic individuals (CPP), who suffer from lower limbs paralysis and anesthesia. To date, the interaction between BR and MSP in paraplegic individuals rem ains unexplored. In two consecutive experiments, we tested I ) if the individual\u2019s wheelchair is embodied in BR; and ii) if the embodied wheelchair modifies the MSP. For the first question a speeded detection task was used. Participants had to respond to v isual stimuli flashing on their trunk, legs or wheelchair. In three counterbalanced conditions across participant, they took part to the experiment while: 1) sitting in their wheelchair, 2) in another wheelchair, or 3) with the LEDs on a wooden bar. To in dicate the embodiment, there was no difference in the CPP\u2019s responses for LEDs on the body and personal wheelchair while these were slower in other conditions After this, while sitting in their or another wheelchair, CPPs were asked to judge the slope of a ramp rendered in immersive virtual reality and to estimate the distance of a flag positioned over the ramp. When on their own wheelchair, CPPs perceived the flag closer than in the other wheelchair. These results indicate that the continuous use of a too l induces embodiment and that this i mpact on the perception of MSP

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations

The knowledge of human mobility is an integral component of several different branches of research and planning, including delay tolerant network routing, cellular network planning, disease prevention, and urban planning. The uncertainty associated with a person's movement plays a central role in movement predictability studies. The uncertainty can be quantified in a succinct manner using entropy rate, which is based on the information theoretic entropy. The entropy rate is usually calculated from past mobility traces. While the uncertainty, and therefore, the entropy rate depend on the human behavior, the entropy rate is not invariant to spatial resolution and sampling interval employed to collect mobility traces. The entropy rate of a person is a manifestation of the observable features in the person's mobility traces. Like entropy rate, these features are also dependent on spatio-temporal quantization. Different mobility studies are carried out using different spatio-temporal quantization, which can obscure the behavioral differences of the study populations. But these behavioral differences are important for population-specific planning. The goal of dissertation is to develop a theoretical model that will address this shortcoming of mobility studies by separating parameters pertaining to human behavior from the spatial and temporal parameters

Training functional mobility using a dynamic virtual reality obstacle course

Falling poses a significant risk of injury for older adults, thus decreasing quality of life. Major risk factors for falling include decrements in gait and balance, and adverse patient-reported health and well-being. Virtual Reality (VR) can be a cost-effective, resource-efficient, and highly engaging training tool, and previous research has utilized VR to reduce fall-risk factors in a variety of populations with aging and pathology. However, there are barriers to implementing VR as a training tool to improve functional mobility in older adults that include the manner in which healthy older adults perform in VR relative to younger adults, the effect of extended duration training, and the relation of fall-risk clinical metrics to performance in VR. The purpose of this dissertation is threefold: (1) to compare performance between older and younger adults in VR and in real-world gait and balance tests as a result of a single bout of VR training; (2) to compare performance in VR and gait and balance within younger adults as a result of extended training duration; and (3) to evaluate clinical tests as prerequisite measures for performance within the VR environment. Thirty-five healthy adults participated in this study and were placed into either the older adult training group (n=8; 67.0±4.4yrs), younger training (n=13; 22.1±2.5yrs), or younger control (n=13; 21.7±1.0yrs). All participants completed an online patient-reported survey of balance confidence and health and well-being, as well as a pre-test of clinical assessments and walking and balance tests. The training groups then completed 15 trials of a VR obstacle course, while the controls walked overground for 15 minutes. The VR obstacle course included a series of gait and dynamic balance tasks, such as stepping on irregularly placed virtual stepping stones and walking a virtual balance beam. All participants repeated the walking and balance tests at post-test. The younger training group also completed 3 weeks of training in the same VR obstacle course and a second post-test. Analyses of variance were completed to determine the extent to which participants improved within VR and the walking and balance tests both as a result of a single bout of training, and for the younger adults – three weeks of extended training. Multiple regressions were run to determine the extent to which patient-reports and clinical assessments may predict performance within VR. The results reported in Manuscript I show that although younger adults completed the VR course quicker, their learning rate was not different from older adults; and as a result of extended training, younger adults continued to improve their time to complete the course. For gait and balance tests, age related differences were observed. Both groups showed better performance on some post-tests, indicating that VR training may have had a positive effect on neuromotor control. The results reported in Manuscript II suggest the RAND-1 pain subscale and simple reaction time (SRT) may predict time to complete the VR course, and SRT and BBS Q14 may additionally predict obstacle contact. These data suggest a VR obstacle course may be effective in improving gait and balance in both younger and older adults. It is recommended that future work enroll older adults in the extended training portion of the study and to increase the VR obstacle course difficulty when benchmarks are met

Do you have to say it? : reflection versus expression in committed relationships

The find-remind-and-bind (Algoe, 2013) theory of gratitude hypothesizes gratitude is not only beneficial intrapersonally but also interpersonally as it brings our attention to the positive attributes of others. However, despite evidence that gratitude may benefit relationships, a limited number of studies have examined how gratitude could be used as a couple intervention. The current experimental couples study compared outcomes of a reflective, intrapersonal journaling gratitude intervention, an expressed, interpersonal gratitude condition, and a control condition. Heterosexual married and cohabitating couples were recruited via social media to participate in a randomly assigned intervention for two weeks. Couples completed pre- and post-intervention assessments of gratitude, relationship satisfaction, and relationship maintenance behaviors, creating two time points of between-group data collection. Multilevel modeling procedures were used to analyze the data, following previous recommendations on dyadic data (Kenny, Kashy, & Cook, 2006). Couples in the expression and reflection groups were found to have greater relationship satisfaction outcomes than the control group. Relationship maintenance behaviors and grateful mood were not impacted by the intervention. Further, there were no detectable differences between couples’ outcomes in the expression group versus the reflection group. The findings contributed to the validity of gratitude as a relational intervention, and the utilization of gratitude as a possible supplement to empirically validated couples therapies is discussed.Thesis (Ph. D.