A major public health challenge is to develop large-scale health communication interventions that are successful with diverse and vulnerable audiences. Participatory design approaches are critical to create communication programs that are relevant to people’s literacy, language, culture, access and functional needs. Further, there are powerful synergies in linking participatory design and artificial intelligence methods. This paper focuses on traditional weaknesses of health communication, and participatory design strategies and models that can be used by developers, researchers and health practitioners.

This paper describes PATSy, an online repository of virtual patient cases for training and research for >students and practitioners in the clinical sciences. A typical student session with PATSy is illustrated. An extension to PATSy that adds vicarious learning resources (VL-PATSy) is also described. The concept of vicarious learning is outlined and results from a study of learning outcomes from VL-PATSy are presented. PATSy and VL-PATSy will be demonstrated at the symposium.

After a hospitalization, approximately 1 out of 5 patients will suffer from an adverse event, and one-third of these complications are preventable. Having a pharmacist follow-up with patients a few days after leaving the hospital has been shown to significantly reduce re-hospitalizations and adverse drug events. In this work, we describe our design for an Embodied Conversational Agent system for longitudinal, post-hospitalization follow-up. We discuss how best-practice follow-up interactions between patients and clinical pharmacists inform the design of our system, focusing on the strategies used by the pharmacist to detect and resolve issues that have occurred post-hospitalization.

Design of effective health communication systems faces major challenges in terms of accessibility, trust, expert-to-lay knowledge translation, and persuasiveness. It is proposed that some of these challenges can be addressed by use of AI techniques in combination with empirically-based theoretical frameworks from the field of health communication and related areas. This symposium will bring together an interdisciplinary group of scholars to identify possible solutions. AI and health communication topics of interest include communication interventions; games, conversational agents, or dialogue systems for healthy behavior promotion; intelligent interactive monitoring of patient's environment and needs; intelligent interfaces supporting access to healthcare; patient-tailored decision support, explanation for informed consent, and retrieval and summarization of online healthcare information; risk communication and visualization; tailored access to electronic medical records; tailoring health information for low-literacy, low-numeracy, or under-served audiences; virtual healthcare counselors; and virtual patients for training healthcare professionals. Scholars from health communication and related disciplines (sociolinguistics, pragmatics, discourse studies, etc.) will participate in discussion on the following issues as they pertain to the symposium goals: health literacy; healthcare provider-consumer communication, risk communication, including written and visual formats; and use of behavioral, persuasion, and argumentation theories for healthy behavior promotion. By examining these issues, the symposium is expected to lay down conceptual foundations for guiding future advances in AI healthcare systems.

Traditional machine-learned ranking systems for web search are often trained to capture stationary relevance of documents to queries, which has limited ability to track non-stationary user intention in a timely manner. In recency search, for instance, the relevance of documents to a query on breaking news often changes significantly over time, requiring effective adaptation to user intention. In this paper, we focus on recency search and study a number of algorithms to improve ranking results by leveraging user click feedback. Our contributions are three-fold. First, we use real search sessions collected in a random exploration bucket for \emph{reliable} offline evaluation of these algorithms, which provides an unbiased comparison across algorithms without online bucket tests. Second, we propose a re-ranking approach to improve search results for recency queries using user clicks. Third, our empirical comparison of a dozen algorithms on real-life search data suggests importance of a few algorithmic choices in these applications, including generalization across different query-document pairs, specialization to popular queries, and real-time adaptation of user clicks.

We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorithms, permitting a multi-gene analysis that can reveal phenotypic differences even when the individual genes do not exhibit differential expression. Here, we apply the PDM to publicly-available gene expression data sets, and demonstrate that we are able to identify cell types and treatments with higher accuracy than is obtained through other approaches. By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may be used to find sets of mechanistically-related genes that discriminate phenotypes.

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.

Scientific advances over the past 150 years, particularly in the medical field, have allowed the extension of life expectancy in western countries and this trend seems to increase in future years. Conservative estimates suggest that by 2030 in EU countries the proportion of people over 60 years regard the entire population will be around 50%; this means that we will see a gradual increase in the number of those subjects with chronic diseases (ie diseases not involving healing), that will therefore increase the cost and effort over health care facilities [1]. As consequence of the exponential growth of hardware and software infrastructure it is possible to rethink the whole approach to the treatment of complex chronic disease, by limiting the hospitalization only to a severe worsening of patient's condition. This was the original idea behind the CHRONIOUS project: constructing a generic platform to monitor, in an unobtrusive way, a chronic disease patient with two goals[2]: - Improve the patients quality of life, by reducing as much as possible the hospitalizations.

The estimation of asset return distributions is crucial for determining optimal trading strategies. One convenient estimation approach selects a distribution model and estimates its parameters. The advantage of this approach is the ease with which probability distributions can be calibrated and applied in post-processing. The disadvantage of assuming a particular parametric distribution is that inferences and decisions depend critically on the choice of distribution. For example, asset returns frequently feature large "outlying" values, making distributions with light tails inapplicable. Semi-parametric methods attempt to capture the advantages but not the disadvantages of a parametric specification of a returns distribution by using a more flexible functional form. Most prominent among the semi-parametric distributions are mixtures of distributions. They provide a flexible specification and, under certain conditions, can approximate distributions of any form.

An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of languages are considered here along with their functions in the mind and cultural evolution. A fundamental contradiction in human mind is considered: language evolution requires reduced emotionality, but "too low" emotionality makes language "irrelevant to life," disconnected from sensory-motor experience. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the language instinct, the dual model connecting language and cognition, dynamic logic, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Approximate equations for evolution of human minds and cultures are obtained. Their solutions identify three types of cultures: "conceptual"-pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; "traditional-emotional" cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and "multi-cultural" societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.