A New Method to Retrieve, Cluster And Annotate Clinical Literature Related To Electronic Health Records

The access to medical literature collections such as PubMed, MedScape or Cochrane has been increased notably in the last years by the web-based tools that provide instant access to the information. However, more sophisticated methodologies are needed to exploit efficiently all that information. The lack of advanced search methods in clinical domain produce that even using well-defined questions for a particular disease, clinicians receive too many results. Since no information analysis is applied afterwards, some relevant results which are not presented in the top of the resultant collection could be ignored by the expert causing an important loose of information. In this work we present a new method to improve scientific article search using patient information for query generation. Using federated search strategy, it is able to simultaneously search in different resources and present a unique relevant literature collection. And applying NLP techniques it presents semantically similar publications together, facilitating the identification of relevant information to clinicians. This method aims to be the foundation of a collaborative environment for sharing clinical knowledge related to patients and scientific publications.

Multivariant non-failure analysis via standard abstract interpretation

Non-failure analysis aims at inferring that predicate calis in a program will never fail. This type of information has many applications in functional/logic programming. It is essential for determining lower bounds on the computational cost of calis, useful in the context of program parallelization, instrumental in partial evaluation and other program transformations, and has also been used in query optimization. In this paper, we re-cast the non-failure analysis proposed by Debray et al. as an abstract interpretation, which not only allows to investígate it from a standard and well understood theoretical framework, but has also several practical advantages. It allows us to incorpórate non-failure analysis into a standard, generic abstract interpretation engine. The analysis thus benefits from the fixpoint propagation algorithm, which leads to improved information propagation. Also, the analysis takes advantage of the multi-variance of the generic engine, so that it is now able to infer sepárate non-failure information for different cali patterns. Moreover, the implementation is simpler, and allows to perform non-failure and covering analyses alongside other analyses, such as those for modes and types, in the same framework. Finally, besides the precisión improvements and the additional simplicity, our implementation (in the Ciao/CiaoPP multiparadigm programming system) also shows better efRciency.

Lower bound cost estimation for logic programs

It is generally recognized that information about the runtime cost of computations can be useful for a variety of applications, including program transformation, granularity control during parallel execution, and query optimization in deductive databases. Most of the work to date on compile-time cost estimation of logic programs has focused on the estimation of upper bounds on costs. However, in many applications, such as parallel implementations on distributed-memory machines, one would prefer to work with lower bounds instead. The problem with estimating lower bounds is that in general, it is necessary to account for the possibility of failure of head unification, leading to a trivial lower bound of 0. In this paper, we show how, given type and mode information about procedures in a logic program, it is possible to (semi-automatically) derive nontrivial lower bounds on their computational costs. We also discuss the cost analysis for the special and frequent case of divide-and-conquer programs and show how —as a pragmatic short-term solution —it may be possible to obtain useful results simply by identifying and treating divide-and-conquer programs specially.

Publishing Orthology and Diseases Information in the Linked Open Data Cloud

The Linked Data initiative offers a straight method to publish structured data in the World Wide Web and link it to other data, resulting in a world wide network of semantically codified data known as the Linked Open Data cloud. The size of the Linked Open Data cloud, i.e. the amount of data published using Linked Data principles, is growing exponentially, including life sciences data. However, key information for biological research is still missing in the Linked Open Data cloud. For example, the relation between orthologs genes and genetic diseases is absent, even though such information can be used for hypothesis generation regarding human diseases. The OGOLOD system, an extension of the OGO Knowledge Base, publishes orthologs/diseases information using Linked Data. This gives the scientists the ability to query the structured information in connection with other Linked Data and to discover new information related to orthologs and human diseases in the cloud.

Estimating the computational cost of logic programs

Information about the computational cost of programs is potentially useful for a variety of purposes, including selecting among different algorithms, guiding program transformations, in granularity control and mapping decisions in parallelizing compilers, and query optimization in deductive databases. Cost analysis of logic programs is complicated by nondeterminism: on the one hand, procedures can return múltiple Solutions, making it necessary to estímate the number of solutions in order to give nontrivial upper bound cost estimates; on the other hand, the possibility of failure has to be taken into account while estimating lower bounds. Here we discuss techniques to address these problems to some extent.

Some improvements over the continuation call tabling implementation technique

Tabled evaluation has been proved an effective method to improve several aspeets of goal-oriented query evaluation, including termination and complexity. Several "native" implementations of tabled evaluation have been developed which offer good performance, but many of them need significant changes to the underlying Prolog implementation. More portable approaches, generally using program transformation, have been proposed but they often result in lower efficieney. We explore some techniques aimed at combining the best of these worlds, i.e., developing a portable and extensible implementation, with minimal modifications at the abstract machine level, and with reasonably good performance. Our preliminary results indícate promising results.

Deriving a fixpoint computation algorithm for top-down abstract interpretation of logic programs

Bruynooghe described a framework for the top-down abstract interpretation of logic programs. In this framework, abstract interpretation is carried out by constructing an abstract and-or tree in a top-down fashion for a given query and program. Such an abstract interpreter requires fixpoint computation for programs which contain recursive predicates. This paper presents in detail a fixpoint algorithm that has been developed for this purpose and the motivation behind it. We start off by describing a simple-minded algorithm. After pointing out its shortcomings, we present a series of refinements to this algorithm, until we reach the final version. The aim is to give an intuitive grasp and provide justification for the relative complexity of the final algorithm. We also present an informal proof of correctness of the algorithm and some results obtained from an implementation.

Ontology-based access to sensor data streams

Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. Our specific contributions are: • A representation of sensor data time series that captures gradient information that is useful to characterize types of sensor data. • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series.

A Metadirectory of Web Components for Mashup Composition

Because of the growing availability of third-party APIs, services, widgets and any other reusable web component, mashup developers now face a vast amount of candidate components for their developments. Moreover, these components quite often are scattered in many different repositories and web sites, which makes difficult their selection or discovery. In this paper, we discuss the problem of component selection in Service-Oriented Architectures (SOA) and Mashup-Driven Development, and introduce the Linked Mashups Ontology (LiMOn), a model that allows describing mashups and their components for integrating and sharing mashup information such as categorization or dependencies. The model has allowed the building of an integrated, centralized metadirectory of web components for query and selection, which has served to evaluate the model. The metadirectory allows accessing various heterogeneous repositories of mashups and web components while using external information from the Linked Data cloud, helping mashup development.

Design and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications?it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ?Activity Monitor? has been designed and implemented: a personal health-persuasive application that provides feedback on the user?s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user?s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results: We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions: CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.