We propose to improve the accuracy of prediction market forecasts by using Bayesian networks to constrain probabilities among related questions. Prediction markets are already known to increase forecast accuracy compared to single best estimates. Our own flat prediction market substantially beat a baseline linear opinion pool during the first year. One way to improve performance is by expressing relationships among the questions. Elsewhere we describe work on combinatorial markets. Here we show how to use Bayesian networks within a flat market. The general approach is to decompose a target question (hypothesis) into a set of related variables (causal factors and evidence), when the relationship among the variables is known with some confidence. Then the marginal probabilities for the variables in the Bayes net are updated using the market estimates, with the Bayes net enforcing coherence. This paper describes the overall concept, shows the results for a particular model of the potential Greek exit from the European Union, and describes the team’s future research plan.

This article provides an overview of BioASQ, a new competition on biomedical semantic indexing and question answering (QA). BioASQ aims to push towards systems that will allow biomedical workers to express their information needs in natural language and that will return concise and user-understandable answers by combining information from multiple sources of different kinds, including biomedical articles, databases, and ontologies. BioASQ encourages participants to adopt semantic indexing as a means to combine multiple information sources and to facilitate the matching of questions to answers. It also adopts a broad semantic indexing and QA architecture that subsumes current relevant approaches, even though no current system instantiates all of its components. Hence, the architecture can also be seen as our view of how relevant work from fields such as information retrieval, hierarchical classification, question answering, ontologies, and linked data can be combined, extended, and applied to biomedical question answering. BioASQ will develop publicly available benchmarks and it will adopt and possibly refine existing evaluation measures. The evaluation infrastructure of the competition will remain publicly available beyond the end of BioASQ.

Previous systems for literature based discovery, an automatic method of identifying hidden knowledge, have largely been based on bag of words approaches which perform only limited semantic analysis and interpretation. We describe the shortcomings of these approaches and suggest possible solutions that make use of techniques from Natural Language Processing.

This extended abstract presents a work in progress of using terminological resources from the biomedical domain to systematically study the change of domain terminology over time. In particular we investigate term replacement. In order to study term replacement over time, semantic knowledge like conceptual granularity of a term is necessary. We analyze three popular biomedical terminology resources (UMLS, CTD, SNOMED CT) and show how information provided there can be used to extract lexically distinctive synonym sets that exclude variants. We use the entire PubMed dataset to chronologically study occurrences of extracted synonyms. Our experiments on the disease subsets of three terminologies reveal that the phenomenon of term replacement can be observed in around 60% of the extracted synonym sets.

In this paper we describe the architecture of the OntoGene Relation mining pipeline and some of its recent applications. With this research overview paper we intend to provide a contribution towards the recently started discussion towards standards for information extraction architectures in the biomedical domain. Our approach delivers domain entities mentioned in each input document, as well as candidate relationships, both ranked according to a confidency score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation.

Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources. In this work we support such research through the use of multi-dimensional latent text models, such as factorial LDA, that capture orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interests to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components. The resulting model learns factors that correspond to drug type, delivery method (smoking, injection, etc.), and aspect (chemistry, culture, effects, health, usage). We demonstrate that the improved model yields better quantitative and more interpretable results.

Current efforts aim to incorporate knowledge from clinical practice guidelines (CPGs) into computer systems using sophisticated interchange formats. Due to their complexity, such formats require expensive manual formalization work. This paper presents a preliminary study of using natural language processing (NLP) to automatically formalize CPG recommendations. We developed a CPG representation using concepts from the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED–CT), and manually applied this representation to a sample of CPG recommendations that is representative of multiple medical domains and recommendation types. Using this resource, we trained and evaluated a supervised classification model that formalizes new CPG recommendations according to the SNOMED–CT representation, achieving a precision of 75% and recall of 42% (F1 = 54%). We have identified two important lines of future investigation: (1) feature engineering to address the unique linguistic properties of CPG recommendations, and (2) alternative model formulations that are more robust to processing errors. A third line of investigation – creating additional training data for the NLP model – is shown to be of little utility.

List of organizers of the AAAI Fall Symposium on  Information Retrieval and Knowledge Discovery in Biomedical Text

In practical applications of robot swarms with bio-inspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm. In this paper, we conduct experiments of human-swarm interaction to investigate the effects of communication latency on the performance of a human-swarm system in a swarm foraging task. We develop and investigate the concept of neglect benevolence, where a human operator allows the swarm to evolve on its own and stabilize before giving new commands. Our experimental results indicate that operators exploited neglect benevolence in different ways to develop successful strategies in the foraging task. Furthermore, we show experimentally that the use of a predictive display can help mitigate the adverse effects of communication latency.

One common use for swarming agents is in social simulation. This paper reports on such a model developed to track protest activities at the May 2012 NATO summit in Chicago. The use of apoptotic stigmergic agents allows the model to run on-line, consuming two kinds of external data and reporting its results in real time.