Design and development of a linked open data-based health information representation and visualization system: potentials and preliminary evaluation

Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases

INTRODUCTION

Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as 'linked' resources based on the Resource Description Framework (RDF). Making resources 'linkable' to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement.

METHODS

Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema.

RESULTS

For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the 'owl:ObjectProperty' type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources.

CONCLUSION

We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.

Systematizing Information Use to Address Determinants of Health Worker Health in South Africa: A Cross-sectional Mixed Method Study

Background

Recognizing that access to safe and healthy working conditions is a human right, the World Health Organization (WHO) calls for specific occupational safety and health (OSH) programs for health workers (HWs). The WHO health systems' building blocks, and the International Labour Organization (ILO), highlight the importance of information as part of effective systems. This study examined how OSH stakeholders access, use, and value an occupational health information system (OHIS).

Methods

A cross-sectional survey of OSH stakeholders was conducted as part of a larger quasi experimental study in four teaching hospitals. The study hospitals and participants were purposefully selected and data collected using a modified questionnaire with both closed and open-ended questions. Quantitative analysis was conducted and themes identified for qualitative analysis. Ethics approval was provided by the University of Pretoria and University of British Columbia.

Results

There were 71 participants comprised of hospital managers, health and safety representatives, trade unions representatives and OSH professionals. At least 42% reported poor accessibility and poor timeliness of OHIS for decision-making. Only 50% had access to computers and 27% reported poor computer skills. When existing, OHIS was poorly organized and needed upgrades, with 85% reporting the need for significant reforms. Only 45% reported use of OHIS for decision-making in their OSH role.

Conclusion

Given the gap in access and utilization of information needed to protect worker's rights to a safe and healthy workplace, more attention is warranted to OHIS development and use as well as education and training in South Africa and beyond.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

Background

Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.

Objective

This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).

Methods

Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.

Results

After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.

Conclusions

With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.

International Registered Report Identifier (IRRID)

RR2-10.2196/14019

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

UMLS to DBPedia link discovery through circular resolution

Objective

The goal of this work is to map Unified Medical Language System (UMLS) concepts to DBpedia resources using widely accepted ontology relations from the Simple Knowledge Organization System (skos:exactMatch, skos:closeMatch) and from the Resource Description Framework Schema (rdfs:seeAlso), as a result of which a complete mapping from UMLS (UMLS 2016AA) to DBpedia (DBpedia 2015-10) is made publicly available that includes 221 690 skos:exactMatch, 26 276 skos:closeMatch, and 6 784 322 rdfs:seeAlso mappings.

Methods

We propose a method called circular resolution that utilizes a combination of semantic annotators to map UMLS concepts to DBpedia resources. A set of annotators annotate definitions of UMLS concepts returning DBpedia resources while another set performs annotation on DBpedia resource abstracts returning UMLS concepts. Our pipeline aligns these 2 sets of annotations to determine appropriate mappings from UMLS to DBpedia.

Results

We evaluate our proposed method using structured data from the Wikidata knowledge base as the ground truth, which consists of 4899 already existing UMLS to DBpedia mappings. Our results show an 83% recall with 77% precision-at-one (P@1) in mapping UMLS concepts to DBpedia resources on this testing set.

Conclusions

The proposed circular resolution method is a simple yet effective technique for linking UMLS concepts to DBpedia resources. Experiments using Wikidata-based ground truth reveal a high mapping accuracy. In addition to the complete UMLS mapping downloadable in n-triple format, we provide an online browser and a RESTful service to explore the mappings.

Predicting Stroke from Electronic Health Records

Studies have identified various risk factors associated with the onset of stroke in an individual. Data mining techniques have been used to predict the occurrence of stroke based on these factors by using patients' medical records. However, there has been limited use of electronic health records to study the inter-dependency of different risk factors of stroke. In this paper, we perform an analysis of patients' electronic health records to identify the impact of risk factors on stroke prediction. We also provide benchmark performance of the state-of-art machine learning algorithms for predicting stroke using electronic health records.

Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

GIS Based Health Information Management through LETL, Multi Criteria Query, Analysis, Visualization

For effective decision making in public health information management(HIM) system, health information availability, accessibility, prompt exchange, GIS linkage, spatiotemporal analysis of diseases is crucial. Lack of cost-effective technical support and information gaps are the main obstacles in HIM. This article defines a generic conceptual process framework for effective HIM that provides cost-effective, portable, easy to use solution. The solution incorporates GIS, Mobile technology, information management concepts, ICD-10 codes, WHO and mHealth standards. The current research is implemented as an android application that facilitates: 1) Patient disease data collection, geospatial mapping of disease data and accumulate a centralized server 2) LETL that supports bulk disease data upload 3) Addresses syntactic and semantic heterogeneity in health data 4) A strong multi-criteria query engine, visualization and spatiotemporal analysis of diseases are designed with a global perspective to be used across the globe.

Open data and public health

This article provides an overview of the intersection of open data and public health by first defining open government data, public health data, and other key concepts and relevant terminologies. There are differing perceptions on the urgency and importance of the openness of public health data. It has been established that disease outbreaks such as happened during the Ebola and Zika virus epidemics are indicative of the need for countries to develop a framework that will provide guidance for the management of public health data. Such a framework should ensure that data collected during public health emergencies are accessible to the appropriate authorities and in a form that can help with timely decision-making during such public health crises. In this article, we highlight available open data policies across many countries, including in the Americas. Our analysis shows that there are currently no articulated policy guidelines for the collection and management of public health data across many countries, especially in Latin America. We propose that any national data governance strategy must address potential benefits, possible risks, examples of data that could be shared, and the attributes of such data. Finally, we stress that the key concern in the Americas should be the development of regional frameworks for open data in public health that can be adopted or adapted by each country through appropriate national or subnational policies and strategies.

Disability and health outcomes in geospatial analyses of Southeastern U.S. county health data

BACKGROUND

People with disabilities tend to be at risk for secondary conditions. There is a need for comprehensive disability and health databases, including geographic information systems to evaluate trends in health, functioning, and employment.

OBJECTIVE

We evaluated county levels in morbidity and mortality across the Southeastern United States using spatial regression, examining 2015 trends in accordance with Healthy People 2020 objectives.

METHODS

We merged 2015 National County Health Rankings and the 2015 Social Security Administration's Report on SSDI Beneficiaries, all for n = 1387 Southeastern U.S. county units. We used GeoDa to regress health and disability multivariable models for the dependent variable, age-adjusted Years of Potential Life Lost (YPLL) per 100,000 population.

RESULTS

The principal Health/Demographic multivariable model of factors impacting YPLL yielded an adjusted R² = 0.743 (F = 188.3, p < 0.001) with percentage physically inactive, preventable hospital stays, percentage diabetics, and low college attendance figuring prominently. A Socioeconomic/Demographic multivariable model impacting YPLL yielded R² = 0.631 (F = 156.0, p < 0.001), with disability and percentage unemployment being major associated variables.

CONCLUSIONS

For the Southeastern U.S., counties with higher prevalence of SSDI disability workers correlated with significantly higher YPLL and poorer health outcomes. The research augments CDC Disability and Health GIS systems to measure Healthy People 2020 outcomes for persons with disabilities nationwide. Spatial regression represents a robust approach for improved analysis of geographic data for population health measures.