Robotic Faces: Exploring Dynamical Patterns of Social Interaction between Humans and Robots

Thesis (Ph.D.) - Indiana University, Informatics, 2015The purpose of this dissertation is two-fold: 1) to develop an empirically-based design for an interactive robotic face, and 2) to understand how dynamical aspects of social interaction may be leveraged to design better interactive technologies and/or further our understanding of social cognition. Understanding the role that dynamics plays in social cognition is a challenging problem. This is particularly true in studying cognition via human-robot interaction, which entails both the natural social cognition of the human and the “artificial intelligence” of the robot. Clearly, humans who are interacting with other humans (or even other mammals such as dogs) are cognizant of the social nature of the interaction – their behavior in those cases differs from that when interacting with inanimate objects such as tools. Humans (and many other animals) have some awareness of “social”, some sense of other agents. However, it is not clear how or why. Social interaction patterns vary across culture, context, and individual characteristics of the human interactor. These factors are subsumed into the larger interaction system, influencing the unfolding of the system over time (i.e. the dynamics). The overarching question is whether we can figure out how to utilize factors that influence the dynamics of the social interaction in order to imbue our interactive technologies (robots, clinical AI, decision support systems, etc.) with some "awareness of social", and potentially create more natural interaction paradigms for those technologies. In this work, we explore the above questions across a range of studies, including lab-based experiments, field observations, and placing autonomous, interactive robotic faces in public spaces. We also discuss future work, how this research relates to making sense of what a robot "sees", creating data-driven models of robot social behavior, and development of robotic face personalities

A Reanalysis of Eurasian Population History: Ancient DNA Evidence of Population Affinities

Mitochondrial hypervariable region I genetic data from ancient populations at two sites from Asia, Linzi in Shandong (northern China) and Egyin Gol in Mongolia, were reanalyzed to detect population affinities. Data from a total of 51 modern populations were used to generate distance measures (Fst's) to the two ancient populations. The tests first analyzed relationships at the regional level, and then compiled the top regional matches for an overall comparison to the two probe populations. The reanalysis showed that the Egyin Gol and Linzi populations have clear distinctions in genetic affinity. The Egyin Gol population as a whole appears to bear close affinities with modern populations of northern East Asia. The Linzi population does seem to have some genetic affinities with the West as suggested by the original analysis, though the original attribution of "European-like" seems to be misleading. This study suggests that the Linzi individuals are potentially related to early Iranians, who are thought to have been widespread in parts of Central Eurasia and the steppe regions in the first millennium BC, though some significant admixture between a number of populations of varying origin cannot be ruled out. The study also examines the effect of sequence length on this type of genetic data analysis and provides analysis and explanation for the results of previous studies on the Linzi sample as compared to this one.Comment: Keywords: d-loop, China, Mongolia, aDNA, mtDNA, Irania

EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that "learn" over time.Comment: Keywords: Data Mining; Decision Support Systems, Clinical; Electronic Health Records; Implementation; Evidence-Based Medicine; Data Warehouse; (2012). EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect. Health Policy and Technology. arXiv admin note: substantial text overlap with arXiv:1112.166

Utilizing RxNorm to Support Practical Computing Applications: Capturing Medication History in Live Electronic Health Records

RxNorm was utilized as the basis for direct-capture of medication history data in a live EHR system deployed in a large, multi-state outpatient behavioral healthcare provider in the United States serving over 75,000 distinct patients each year across 130 clinical locations. This tool incorporated auto-complete search functionality for medications and proper dosage identification assistance. The overarching goal was to understand if and how standardized terminologies like RxNorm can be used to support practical computing applications in live EHR systems. We describe the stages of implementation, approaches used to adapt RxNorm's data structure for the intended EHR application, and the challenges faced. We evaluate the implementation using a four-factor framework addressing flexibility, speed, data integrity, and medication coverage. RxNorm proved to be functional for the intended application, given appropriate adaptations to address high-speed input/output (I/O) requirements of a live EHR and the flexibility required for data entry in multiple potential clinical scenarios. Future research around search optimization for medication entry, user profiling, and linking RxNorm to drug classification schemes holds great potential for improving the user experience and utility of medication data in EHRs.Comment: Appendix (including SQL/DDL Code) available by author request. Keywords: RxNorm; Electronic Health Record; Medication History; Interoperability; Unified Medical Language System; Search Optimizatio

Physical activity in US Blacks: a systematic review and critical examination of self-report instruments

<p>Abstract</p> <p>Background</p> <p>Physical activity self-report instruments in the US have largely been developed for and validated in White samples. Despite calls to validate existing instruments in more diverse samples, relatively few instruments have been validated in US Blacks. Emerging evidence suggests that these instruments may have differential validity in Black populations.</p> <p>Purpose</p> <p>This report reviews and evaluates the validity and reliability of self-reported measures of physical activity in Blacks and makes recommendations for future directions.</p> <p>Methods</p> <p>A systematic literature review was conducted to identify published reports with construct or criterion validity evaluated in samples that included Blacks. Studies that reported results separately for Blacks were examined.</p> <p>Results</p> <p>The review identified 10 instruments validated in nine manuscripts. Criterion validity correlations tended to be low to moderate. No study has compared the validity of multiple instruments in a single sample of Blacks.</p> <p>Conclusion</p> <p>There is a need for efforts validating self-report physical activity instruments in Blacks, particularly those evaluating the relative validity of instruments in a single sample.</p