From Cues to Nudge: A Knowledge-Based Framework for Surveillance of Healthcare-Associated Infections.

We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guidelines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections - Knowledge in Use) framework enables hospitals to consistently follow the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relationships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection.

Benchmarking infrastructure for mutation text mining.

Background: Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems.

Results: We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system.

Semantic querying of relational data for clinical intelligence: a semantic web services-based approach.

Background: Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Self-service ad hoc querying of clinical data is one desirable type of functionality. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas.

HAIKU: A Semantic Framework for Surveillance of Healthcare-Associated Infections.

Healthcare-Associated Infections (HAI) impose a substantial health and financial burden. Surveillance for HAI is essential to develop and evaluate prevention and control efforts. The traditional approaches to HAI surveillance are often limited in scope and efficiency by the need to manually obtain and integrate data from disparate paper charts and information systems.

Deploying mutation impact text-mining software with the SADI Semantic Web Services framework.

Background: Mutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining.

Results: This article explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data.

Knowledge-based surveillance for preventing postoperative surgical site infection.

At least one out of every twenty people admitted to a Canadian hospital will acquire an infection. These hospital-acquired infections (HAIs) take a profound individual and system-wide toll, resulting in thousands of deaths and hundreds of millions of dollars in additional expenses each year. Surveillance for HAIs is essential to develop and evaluate prevention and control efforts.

Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics.

Background: The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality.

Algorithms and semantic infrastructure for mutation impact extraction and grounding.

Background: Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases.

Results: We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral.