On the usefulness of ontologies in epidemiology research and practice

Ferreira, João D.; Paolotti, Daniela; Couto, Francisco M.; Silva, Mário J.

doi:10.1136/jech-2012-201142

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…Although we did not find an association between history of psychiatric care and early suicide, psychological stress induced by a cancer diagnosis can result in the development of psychiatric disorders such as depressive disorder, anxiety disorder, and acute stress disorder, and this stress can have an impact on suicide mortality both directly and indirectly via development of psychiatric disorder . In particular, patients who are diagnosed with distant metastasis or terminal‐stage disease experience a high level of distress and have a high risk for the development of psychiatric disorders .…”

Section: Discussioncontrasting

confidence: 59%

Suicide in cancer patients within the first year of diagnosis

et al. 2014

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Section: Discussioncontrasting

confidence: 59%

Suicide in cancer patients within the first year of diagnosis

et al. 2014

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“…In computer science, an ontology is a formal, explicit specification of a shared conceptualization [ 36 ]; ontologies are widely used in artificial intelligence, the semantic Web, and biomedical informatics as a form of knowledge representation. Formal approaches, like ontologies and the semantic Web, are important instruments also in epidemiology research [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

FRAT-up, a Fall-Risk Assessment Tool for Elderly People Living in the Community

Cattelani

Palumbo²,

Palmerini³

et al. 2015

J Med Internet Res

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BackgroundAbout 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls.ObjectiveThe goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up.MethodsFRAT-up is based on the assumption that a subject’s fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators.ResultsThe obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration.ConclusionsFRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface.Trial RegistrationClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).

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“…Unlike the knowledge concealed in the articles, the knowledge shared through annotations using standard ontologies can be easily processed and analyzed by computational methods. For example, it enables us to search for similar and related entities based on their biomedical meaning, such as similar molecular functions and similar diseases [46].…”

Section: Discussionmentioning

confidence: 99%

Knowledge Representation and Management: a Linked Data Perspective

Barros¹,

Couto²

2016

Yearb Med Inform

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Summary Introduction: Biomedical research is increasingly becoming a data-intensive science in several areas, where prodigious amounts of data is being generated that has to be stored, integrated, shared and analyzed. In an effort to improve the accessibility of data and knowledge, the Linked Data initiative proposed a well-defined set of recommendations for exposing, sharing and integrating data, information and knowledge, using semantic web technologies. Objective: The main goal of this paper is to identify the current status and future trends of knowledge representation and management in Life and Health Sciences, mostly with regard to linked data technologies. Methods: We selected three prominent linked data studies, namely Bio2RDF, Open PHACTS and EBI RDF platform, and selected 14 studies published after 2014 (inclusive) that cited any of the three studies. We manually analyzed these 14 papers in relation to how they use linked data techniques. Results: The analyses show a tendency to use linked data techniques in Life and Health Sciences, and even if some studies do not follow all of the recommendations, many of them already represent and manage their knowledge using RDF and biomedical ontologies. Conclusion: These insights from RDF and biomedical ontologies are having a strong impact on how knowledge is generated from biomedical data, by making data elements increasingly connected and by providing a better description of their semantics. As health institutes become more data centric, we believe that the adoption of linked data techniques will continue to grow and be an effective solution to knowledge representation and management.

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On the usefulness of ontologies in epidemiology research and practice

Cited by 14 publications

References 19 publications

Suicide in cancer patients within the first year of diagnosis

Suicide in cancer patients within the first year of diagnosis

FRAT-up, a Fall-Risk Assessment Tool for Elderly People Living in the Community

Knowledge Representation and Management: a Linked Data Perspective

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