Health Care And Life Sciences Research Articles

Blockchain: An enabler for healthcare and life sciences transformation

Major trends in healthcare and life sciences (HCLS) include huge amounts of and longitudinal patient data, policies on a patient's rights to access and control their data, a move from fee-for-service to outcome-based contracts, and regulatory and privacy requirements. Blockchain, as a distributed transactional system of record, can provide underpinnings to enable these trends and enable transformative opportunities in HCLS by providing immutable data on a shared ledger, secure and authenticated transactions, and smart contracts that can represent rules that are executed with secure transactions. We describe HCLS use cases leveraging these facets of blockchain, including patient consent and health data exchange, outcome-based contracts, next-generation clinical trials, supply chain, and payments and claims. We then describe a blockchain-based architecture and platform for enabling these use cases. Finally, we outline a realization of this architecture in a case study and outline further research topics in this domain.

SAFE: SPARQL Federation over RDF Data Cubes with Access Control

BackgroundSeveral query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data.ResultsWe present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change.ConclusionsWe validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

A fine-grained context-aware access control model for health care and life science linked data

Health Care and Life Sciences (HCLS) have long been a test-bed for the standards proposed by the W3C to build the Semantic Web. One of the challenges to HCLS Linked Data is access control. In this paper, we present a fine-grained context-aware access model for HCLS Linked Data based on Semantic Web tools. The model consists of two basic components: ontology base and access policy. Ontology base refers to a set of ontologies that include subject ontology, resource ontology, environment ontology, and action ontology. In the access policy module, we describe the access policy with eXtensible Access Control Markup Language (XACML) model, which allows users to achieve access rule reproduction by defining the semantic scope and inference rules among different entities. Results of the analysis indicate that indicates that our model expands the scopes of authorization rules for users. Inference of semantic authorization rules is also realized. These rules enable fine-grained access to data and meet the need for dynamic change of HCLS Linked Data. Finally, we show the process of authorization and present a system framework. Simulation experiments verify the acceptability of our model in protecting secured data.

Special issue on Linked Data for Health Care and the Life Sciences

Health Care and Life Sciences (HCLS) have long been a test-bed for the standards proposed by the W3C to build the Semantic Web1: since HCLS is descriptive by nature and its descriptions have traditionally been produced according to ad-hoc schemas in isolated resources, HCLS offers an ideal use case for technologies like RDF2, SPARQL3 and OWL4 [1,4]. This “marriage” of the HCLS domain with semantic technologies has resulted in a collection of resources that can be regarded as an HCLS-focused working implementation of the idea of the Semantic Web: the socalled Life Sciences Semantic Web (LSSW). As part of the process of implementing the LSSW, the HCLS community has adopted the Linked Data practices to publish information in a machine-friendly and linkable fashion [3], as a “down-to-earth” version of a prospective fully-fledged Semantic Web. This has resulted in members of the HCLS community, like the W3C HCLS Interest Group5, considerably contributing to the Linked Open Data (LOD) endeavour, with datasets like Bio2RDF [2] and Linked Open Drug Data (LODD) [5]. As the LOD network grows, producers and consumers alike are facing new challenges regarding interoperable vocabularies, filtering, graphical interfaces,

Translating standards into practice – One Semantic Web API for Gene Expression

Sharing and describing experimental results unambiguously with sufficient detail to enable replication of results is a fundamental tenet of scientific research. In today’s cluttered world of “-omics” sciences, data standards and standardized use of terminologies and ontologies for biomedical informatics play an important role in reporting high-throughput experiment results in formats that can be interpreted by both researchers and analytical tools. Increasing adoption of Semantic Web and Linked Data technologies for the integration of heterogeneous and distributed health care and life sciences (HCLSs) datasets has made the reuse of standards even more pressing; dynamic semantic query federation can be used for integrative bioinformatics when ontologies and identifiers are reused across data instances. We present here a methodology to integrate the results and experimental context of three different representations of microarray-based transcriptomic experiments: the Gene Expression Atlas, the W3C BioRDF task force approach to reporting Provenance of Microarray Experiments, and the HSCI blood genomics project. Our approach does not attempt to improve the expressivity of existing standards for genomics but, instead, to enable integration of existing datasets published from microarray-based transcriptomic experiments. SPARQL Construct is used to create a posteriori mappings of concepts and properties and linking rules that match entities based on query constraints. We discuss how our integrative approach can encourage reuse of the Experimental Factor Ontology (EFO) and the Ontology for Biomedical Investigations (OBIs) for the reporting of experimental context and results of gene expression studies.

Clinical Genomics Data Standards for Pharmacogenetics and Pharmacogenomics

This special report concerns a talk on data standards given at a workshop entitled 'An International Perspective on Pharmacogenetics: The Intersections between Innovation, Regulation and Health Delivery', which was held by the Organization for Economic Co-operation and Development (OECD) on October 17-19, 2005, in Rome, Italy. The worlds of healthcare and life sciences (HCLS) are extremely fragmented in terms of their underlying information technology, making it difficult to semantically exchange information between disparate entities. While we have reached the point where functional interoperability is ubiquitous, we are still far from achieving true semantic interoperability where a receiving system can use incoming data as though it was created internally. The critical enablers of semantic interoperability are information standards dedicated to HCLS data, spanning all the way from biological research data to clinical research and clinical trials, and finally to healthcare clinical data. The challenge lies in integrating various data standards based on predetermined goals, thereby improving the quality of care provided to patients.