Linked open data-based framework for automatic biomedical ontology generation.

BMC Bioinformatics. 2018 Sep 10;19(1):319

Authors: Alobaidi M, Malik KM, Sabra S

Abstract
BACKGROUND: Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns.
RESULTS: Our evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontology-learning framework called "OntoGain" which shows that LOD-ABOG performs 14.76% better in terms of relation extraction.
CONCLUSION: This paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.

PMID: 30200874 [PubMed - in process]

Representing vaccine misinformation using ontologies.

J Biomed Semantics. 2018 Aug 31;9(1):22

Authors: Amith M, Tao C

Abstract
BACKGROUND: In this paper, we discuss the design and development of a formal ontology to describe misinformation about vaccines. Vaccine misinformation is one of the drivers leading to vaccine hesitancy in patients. While there are various levels of vaccine hesitancy to combat and specific interventions to address those levels, it is important to have tools that help researchers understand this problem. With an ontology, not only can we collect and analyze varied misunderstandings about vaccines, but we can also develop tools that can provide informatics solutions.
RESULTS: We developed the Vaccine Misinformation Ontology (VAXMO) that extends the Misinformation Ontology and links to the nanopublication Resource Description Framework (RDF) model for false assertions of vaccines. Preliminary assessment using semiotic evaluation metrics indicated adequate quality for our ontology. We outlined and demonstrated proposed uses of the ontology to detect and understand anti-vaccine information.
CONCLUSION: We surmised that VAXMO and its proposed use cases can support tools and technology that can pave the way for vaccine misinformation detection and analysis. Using an ontology, we can formally structure knowledge for machines and software to better understand the vaccine misinformation domain.

PMID: 30170633 [PubMed - in process]

SNOMED CT standard ontology based on the ontology for general medical science.

BMC Med Inform Decis Mak. 2018 Aug 31;18(1):76

Authors: El-Sappagh S, Franda F, Ali F, Kwak KS

Abstract
BACKGROUND: Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT.
METHOD: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS).
RESULTS: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at http://bioportal.bioontology.org/ontologies/SCTO/ .
CONCLUSION: The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.

PMID: 30170591 [PubMed - in process]

A Method to Use Metadata in Legacy Web Applications: The Samply.MDR.Injector.

Stud Health Technol Inform. 2018;253:45-49

Authors: Kern J, Tas D, Ulrich H, Schmidt EE, Ingenerf J, Ückert F, Lablans M

Abstract
Whenever medical data is integrated from multiple sources, it is regarded good practice to separate data from information about its meaning, such as designations, definitions or permissible values (in short: metadata). However, the ways in which applications work with metadata are imperfect: Many applications do not support fetching metadata from externalized sources such as metadata repositories. In order to display human-readable metadata in any application, we propose not to change the application, but to provide a library that makes a change to the user interface. The goal of this work is to provide a way to "inject" the meaning of metadata keys into the web-based frontend of an application to make it "metadata aware".

PMID: 30147038 [PubMed - in process]

A Web Service to Suggest Semantic Codes Based on the MDM-Portal.

Stud Health Technol Inform. 2018;253:35-39

Authors: Hegselmann S, Storck M, Geßner S, Neuhaus P, Varghese J, Dugas M

Abstract
Annotation with semantic codes helps to overcome interoperability issues for electronic documentation in medicine. However, the manual annotation process is laborious and semantic codes are ambiguous. We developed a publicly accessible web service to alleviate these drawbacks with a sophisticated and fast search mechanism based on more than 330,000 semantic code suggestions. These suggestions are derived from semantically annotated data elements contained in the Portal of Medical Data Models manually curated by medical professionals. Integrating this suggestion service can support the manual annotation process and strengthen uniform coding. Integration is demonstrated for two separate data model editors. Usage statistics show over 5,500 suggestion requests per month for semantic annotation of items. The web service may also prove helpful for automatic semantic coding.

PMID: 30147036 [PubMed - in process]

The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques.

Med Phys. 2018 Aug 24;:

Authors: Traverso A, van Soest J, Wee L, Dekker A

Abstract
PURPOSE: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appealing, low-cost and noninvasive approach toward personalization. Machine learning algorithms could be trained over clinical "big data" to build prediction models for personalized therapy. To reach this goal, a scalable "big data" architecture for the medical domain becomes essential, based on data standardization to transform clinical data into FAIR (Findable, Accessible, Interoperable and Reusable) data. Using Ontologies and Semantic Web technologies, we attempt to reach mentioned goal.
METHODS: We developed an ontology to be used in the field of radiation oncology to map clinical data from relational databases. We combined ontology with semantic Web techniques to publish mapped data and easily query them using SPARQL.
RESULTS: The Radiation Oncology Ontology (ROO) contains 1,183 classes and 211 properties between classes to represent clinical data (and their relationships) in the radiation oncology domain following FAIR principles. We combined the ontology with Semantic Web technologies showing how to efficiently and easily integrate and query data from different (relational database) sources without a priori knowledge of their structures.
DISCUSSION: When clinical FAIR data sources are combined (linked data) using mentioned technologies, new relationships between entities are created and discovered, representing a dynamic body of knowledge that is continuously accessible and increasing.

PMID: 30144092 [PubMed - as supplied by publisher]

VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning.

IEEE Trans Vis Comput Graph. 2018 Aug 20;:

Authors: Sacha D, Kraus M, Keim DA, Chen M

Abstract
While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely "VA-assisted ML". The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.

PMID: 30130221 [PubMed - as supplied by publisher]

Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients.

Strahlenther Onkol. 2018 06;194(6):580-590

Authors: Peeken JC, Hesse J, Haller B, Kessel KA, Nüsslin F, Combs SE

Abstract
BACKGROUND: For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information.
METHODS: 189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan-Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined.
RESULTS: For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. "Multilocality," "deep white-matter invasion," "satellites," and "ependymal invasion" were over proportionally selected for multivariate model generation, underlining their importance.
CONCLUSIONS: We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

PMID: 29442128 [PubMed - indexed for MEDLINE]

EAPB: entropy-aware path-based metric for ontology quality.

J Biomed Semantics. 2018 Aug 10;9(1):20

Authors: Shen Y, Chen D, Tang B, Yang M, Lei K

Abstract
BACKGROUND: Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.
RESULTS: We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.
CONCLUSIONS: We leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate ).

PMID: 30097014 [PubMed - in process]

Common Consumer Health-Related Needs in the Pediatric Hospital Setting: Lessons from an Engagement Consultation Service.

Appl Clin Inform. 2018 Jul;9(3):595-603

Authors: Lee DJ, Cronin R, Robinson J, Anders S, Unertl K, Kelly K, Hankins H, Skeens R, Jackson GP

Abstract
BACKGROUND:  Informed and engaged parents may influence outcomes for childhood illness. Understanding the needs of the caregivers of pediatric patients is a critical first step in promoting engagement in their child's care. In 2014, we developed an Engagement Consultation Service at the Monroe Carell Jr. Children's Hospital at Vanderbilt. This service determines the health-related needs of the caregivers of hospitalized children and makes educational or technology recommendations to meet those needs and support engagement.
OBJECTIVES:  This report describes the most common health-related needs identified in the caregivers of hospitalized pediatric patients and details the recommended interventions to meet those needs.
METHODS:  The most commonly reported consumer health-related needs from our 3-year experience with the Engagement Consultation Service were extracted from consultations notes. Each need was classified by semantic type using a taxonomy of consumer health needs. Typical recommendations for each need and their administration were detailed.
RESULTS:  The most frequently recognized needs involved communicating with health care providers after discharge, using medical devices, distinguishing between benign and concerning symptoms, knowing what questions to ask providers and remembering them, finding trustworthy sources of information online, understanding disease prognosis, and getting emotional support. A variety of apps, Web sites, printed materials, and online groups were recommended.
CONCLUSION:  The parents of hospitalized patients share several common health-related needs that can be addressed with educational and technology interventions. An inpatient Engagement Consultation Service provides a generalizable framework for identifying health-related needs and delivers tools to meet those needs and promote engagement during and after hospitalizations.

PMID: 30089333 [PubMed - in process]

Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach.

J Integr Bioinform. 2018 Aug 07;:

Authors: Brandizi M, Singh A, Rawlings C, Hassani-Pak K

Abstract
The speed and accuracy of new scientific discoveries - be it by humans or artificial intelligence - depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles).

PMID: 30085931 [PubMed - as supplied by publisher]

An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival.

BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):41

Authors: Zhang H, Guo Y, Li Q, George TJ, Shenkman E, Modave F, Bian J

Abstract
BACKGROUND: Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges.
METHODS: Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs.
RESULTS: Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies.
CONCLUSIONS: Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.

PMID: 30066664 [PubMed - in process]

OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.

BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):55

Authors: Lossio-Ventura JA, Hogan W, Modave F, Guo Y, He Z, Yang X, Zhang H, Bian J

Abstract
BACKGROUND: There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB.
METHODS: We developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB.
RESULTS: We conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB.
CONCLUSIONS: We presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.

PMID: 30066655 [PubMed - in process]

Visualized Emotion Ontology: a model for representing visual cues of emotions.

BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):64

Authors: Lin R, Amith MT, Liang C, Duan R, Chen Y, Tao C

Abstract
BACKGROUND: Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being.
METHODS: In order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors.
RESULTS: As determined by ontology evaluation metrics, VEO exhibited better machine-readability (z=1.12), linguistic quality (z=0.61), and domain coverage (z=0.39) compared to a sample of cognitive ontologies. Additionally, a survey of 1082 participants through Amazon Mechanical Turk revealed that a significantly higher proportion of people agree than disagree with 17 out of our 25 emotion images, validating the majority of our visualizations.
CONCLUSION: From the development, evaluation, and serialization of the VEO, we have defined a set of 25 emotions using OWL that linked surveyed visualizations to each emotion. In the future, we plan to use the VEO in patient-facing software tools, such as embodied conversational agents, to enhance interactions between patients and providers in a clinical environment.

PMID: 30066654 [PubMed - in process]

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment.

J Digit Imaging. 2018 Jul 31;:

Authors: Messaoudi R, Jaziri F, Mtibaa A, Grand-Brochier M, Ali HM, Amouri A, Fourati H, Chabrot P, Gargouri F, Vacavant A

Abstract
Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).

PMID: 30066122 [PubMed - as supplied by publisher]

An Ontology-Based Knowledge Methodology in the Medical Domain in the Latin America: the Study Case of Republic of Panama.

Acta Inform Med. 2018 Jun;26(2):98-101

Authors: Cedeno-Moreno D, Vargas-Lombardo M

Abstract
Introduction: Nowadays in Panama, there is a lot of patient information stored in textual form which cannot be manipulated to manage adequate knowledge. There are multiple resources created to represent knowledge, including specialized glossaries, ontologies, among others. The ontologies are an important part within the scope of the recovery and organization of the information and the semantic web. Also in recent works they are used in applications of natural language processing (NLP), as a knowledge base.
Aim: This research was conducted with the aim of creating a methodology that allows from a text written in NL, extract the necessary elements using NLP tools and with them create a knowledge base represented by one domain ontology and extract knowledge to help medical specialists.
Material and Methods: In this study we carried out a methodology that allows the extraction of knowledge of patient clinical records, general medicine and palliative care, in order to show relevant knowledge elements to specialists. The methodology was validated with a data corpus of approximately 200 patient records.
Conclusion: We have created a knowledge representation methodology, combining NLP techniques and tools and the automatic instantiation of an ontology, which can serve as a software agent for other applications or used to visualize the patient's clinical information. The study was validated using the traditional metrics of information retrieval systems precision, recall, F-measure obtaining excellent results, and can be used as a software agent or methodology for the development of information extraction software systems in the medical domain.

PMID: 30061779 [PubMed]

On the plausibility of socioeconomic mortality estimates derived from linked data: a demographic approach.

Popul Health Metr. 2017 Jul 14;15(1):26

Authors: Lerch M, Spoerri A, Jasilionis D, Viciana Fernandèz F

Abstract
BACKGROUND: Reliable estimates of mortality according to socioeconomic status play a crucial role in informing the policy debate about social inequality, social cohesion, and exclusion as well as about the reform of pension systems. Linked mortality data have become a gold standard for monitoring socioeconomic differentials in survival. Several approaches have been proposed to assess the quality of the linkage, in order to avoid the misclassification of deaths according to socioeconomic status. However, the plausibility of mortality estimates has never been scrutinized from a demographic perspective, and the potential problems with the quality of the data on the at-risk populations have been overlooked.
METHODS: Using indirect demographic estimation (i.e., the synthetic extinct generation method), we analyze the plausibility of old-age mortality estimates according to educational attainment in four European data contexts with different quality issues: deterministic and probabilistic linkage of deaths, as well as differences in the methodology of the collection of educational data. We evaluate whether the at-risk population according to educational attainment is misclassified and/or misestimated, correct these biases, and estimate the education-specific linkage rates of deaths.
RESULTS: The results confirm a good linkage of death records within different educational strata, even when probabilistic matching is used. The main biases in mortality estimates concern the classification and estimation of the person-years of exposure according to educational attainment. Changes in the census questions about educational attainment led to inconsistent information over time, which misclassified the at-risk population. Sample censuses also misestimated the at-risk populations according to educational attainment.
CONCLUSION: The synthetic extinct generation method can be recommended for quality assessments of linked data because it is capable not only of quantifying linkage precision, but also of tracking problems in the population data. Rather than focusing only on the quality of the linkage, more attention should be directed towards the quality of the self-reported socioeconomic status at censuses, as well as towards the accurate estimation of the at-risk populations.

PMID: 28705165 [PubMed - indexed for MEDLINE]

Pharmacovigilance from social media: An improved random subspace method for identifying adverse drug events.

Int J Med Inform. 2018 Sep;117:33-43

Authors: Liu J, Wang G

Abstract
OBJECTIVE: Recent advances in Web 2.0 technologies have seen significant strides towards utilizing patient-generated content for pharmacovigilance. Social media-based pharmacovigilance has great potential to augment current efforts and provide regulatory authorities with valuable decision aids. Among various pharmacovigilance activities, identifying adverse drug events (ADEs) is very important for patient safety. However, in health-related discussion forums, ADEs may confound with drug indications and beneficial effects, etc. Therefore, the focus of this study is to develop a strategy to identify ADEs from other semantic types, and meanwhile to determine the drug that an ADE is associated with.
MATERIALS AND METHODS: In this study, two groups of features, i.e., shallow linguistic features and semantic features, are explored. Moreover, motivated and inspired by the characteristics of explored two feature categories for social media-based ADE identification, an improved random subspace method, called Stratified Sampling-based Random Subspace (SSRS), is proposed. Unlike conventional random subspace method that applies random sampling for subspace selection, SSRS adopts stratified sampling-based subspace selection strategy.
RESULTS: A case study on heart disease discussion forums is performed to evaluate the effectiveness of the SSRS method. Experimental results reveal that the proposed SSRS method significantly outperforms other compared ensemble methods and existing approaches for ADE identification.
DISCUSSION AND CONCLUSION: Our proposed method is easy to implement since it is based on two feature sets that can be naturally derived, and therefore, can omit artificial stratum generation efforts. Moreover, SSRS has great potential of being applied to deal with other high-dimensional problems that can represent original data from two different aspects.

PMID: 30032963 [PubMed - in process]

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization.

IEEE Trans Cybern. 2018 Jul 17;:

Authors: Howell SK, Wicaksono H, Yuce B, McGlinn K, Rezgui Y

Abstract
This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.

PMID: 30028719 [PubMed - as supplied by publisher]

Speed-Dial: A Surrogate Mouse for Non-Visual Web Browsing.

ASSETS. 2017 Oct-Nov;2017:110-119

Authors: Billah SM, Ashok V, Porter DE, Ramakrishnan IV

Abstract
Sighted people can browse the Web almost exclusively using a mouse. This is because web browsing mostly entails pointing and clicking on some element in the web page, and these two operations can be done almost instantaneously with a computer mouse. Unfortunately, people with vision impairments cannot use a mouse as it only provides visual feedback through a cursor. Instead, they are forced to go through a slow and tedious process of building a mental map of the web page, relying primarily on a screen reader's keyboard shortcuts and its serial audio readout of the textual content of the page, including metadata. This can often cause content and cognitive overload. This paper describes our Speed-Dial system which uses an off-the-shelf physical Dial as a surrogate for the mouse for non-visual web browsing. Speed-Dial interfaces the physical Dial with the semantic model of a web page, and provides an intuitive and rapid access to the entities and their content in the model, thereby bringing blind people's browsing experience closer to how sighted people perceive and interact with the Web. A user study with blind participants suggests that with Speed-Dial they can quickly move around the web page to select content of interest, akin to pointing and clicking with a mouse.

PMID: 30027156 [PubMed]