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.

PubMed ® and other literature databases contain a wealth of information on diseases and their diagnosis/treatment in the form of scientific publications. In order to take advantage of such rich information, several text-mining tools have been developed for automatically detecting mentions of disease names in the PubMed abstracts. The next important step is the normalization of the various disease names to standardized vocabulary entries and medical dictionaries. To this end, we present an automatic approach for mapping disease names in PubMed abstracts to their corresponding concepts in Medical Subject Headings (MeSH ® ) or Online Mendelian Inheritance in Man (OMIM ® ). For developing our algorithm, we merged disease concept annotations from two existing corpora. In addition, we hand annotated a separate test set of decease concepts for our method evaluation. Different from others, we reformulate the disease name normalization task as an information retrieval task where input queries are disease names and search results are disease concepts. As such, our inference method builds on existing Lucene search and further improves it by taking into account the string similarity of query terms to the disease concept name and synonyms. Evaluation results show that our method compares favorably to other state-of-the-art approaches. In conclusion, we find that our approach is a simple and effective way for linking disease names to controlled vocabularies and that the merged disease corpus provides added value for the development of text mining tools for named entity recognition from biomedical text. Data is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Fellows/Dogan/disease.html

Social networking websites such as Twitter have invigorated a wide range of studies in recent years ranging from consumer opinions on products to tracking the spread of diseases. While sentiment analysis and opinion mining from tweets have been studied extensively, surveillance of beliefs, especially those related to public health, have received considerably less attention. In our previous work, we proposed a model for surveillance of health beliefs on Twitter relying on the use of hand-picked probe statements expressing various health-related propositions. In this work we extend our model to automatically discover various probes related to public health beliefs. We present a data driven approach based on two distinct datasets and study the prevalence of public belief, disbelief or doubt for newly discovered probe statements.

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.

Robots are increasingly introduced to work in concert with people in high-intensity domains, such as manufacturing, space exploration and hazardous environments. Although there are numerous studies on human teamwork and coordination in these settings, very little prior work exists on applying these models to human-robot interaction. This paper presents results from ongoing work aimed at translating qualitative methods from human factors engineering into computational models that can be applied to human-robot teaming. We describe a statistical approach to learning patterns of strong and weak agreements in human planning meetings that achieves up to 94% prediction accuracy. We also formulate a human-robot interactive planning method that emulates cross-training, a training strategy widely used in human teams. Results from human subject experiments show statistically significant improvements on team fluency metrics, compared to standard reinforcement learning techniques. Results from these two studies support the approach of modeling and applying common practices in human teaming to achieve more effective and fluent human-robot teaming.

Human interaction with robot swarms (HSI) is a young field with very few user studies that explore operator behavior. All these studies assume perfect communication between the operator and the swarm. A key challenge in the use of swarm robotic systems in human supervised tasks is to understand human swarm interaction in the presence of limited communication bandwidth, which is a constraint arising in many practical scenarios. In this paper, we present results of human-subject experiments designed to study the effect of bandwidth limitations in human swarm interaction. We consider three levels of bandwidth availability in a swarm foraging task. The lowest bandwidth condition performs poorly, but the medium and high bandwidth condition both perform well. In the medium bandwidth condition, we display useful aggregated swarm information (like swarm centroid and spread) to compress the swarm state information. We also observe interesting operator behavior and adaptation of operators’ swarm reaction.

In this paper, we investigate what role the network topology plays when controlling a network of mobile robots. This is a question of key importance in the emerging area of human-swarm interaction and we approach this question by letting a human user inject control signals at a single leader-node, which are then propagated throughout the network. Based on a user study, it is found that some topologies are more amenable to human control than others, which can be interpreted in terms of the rank of the controllability matrix of the underlying network dynamics, as well as, measures of node centrality on the leader of the network.

Knowledge engineering from experimental design (KEfED) is a novel approach based on the dependency relationships that occur between the variables of a scientific study. We used this approach to curate the experimental designs of ten scientific papers from a well-established database of HIV vaccine trials in non-human primates. The KEfED models provide a characteristic, data-oriented signature for each measurement made in the study. We present preliminary analysis of these manually-curated, detailed representations using our own open-source curation tools and show the multi-variate statistical analyses on the resultant models of experimental design. The analyses produced a visualization of the similarities between studies and an account of the dependency relationships across studies. We describe our approach in the context of a knowledge engineering strategy based on creating large-scale domain-independent repositories of experimental observatio

In the context of science, abstract workflows can bridge the gap between scientists and technologists towards using computer systems to carry out scientific processes. Provenance traces provide evidence required to validate scientific products and support their use by others. With abstract workflows and provenance traces based on formal semantics, a knowledge-based framework that merges both technologies are devised, allowing scientists to formally document their processes of data collection and transformation and allowing others to use semantic-based technologies to discover and assess data, processes, and derived data products. This paper presents an approach for evaluating the level of scientist support in frameworks that integrate abstract workflows and provenance traces. In order to support discovery of scientific results, it is essential to provide tools for scientists to document the processes they use to obtain the results. The claim is that the complementary technologies of abstract workflows and provenance traces need to be flexible enough to support a scientist’s perspective and minimize imposition of technically-oriented abstractions that may be extraneous to them. The evaluation approach uses criteria that are derived from tasks performed by scientists using both technologies, i.e., process authoring, process analysis, process interoperability, provenance capturing, provenance analysis, and provenance interoperability.