• Corpus ID: 53832600

Semantically-aware population health risk analyses

@article{New2018SemanticallyawarePH,
  title={Semantically-aware population health risk analyses},
  author={Alexander New and Sabbir M. Rashid and John S. Erickson and Deborah L. McGuinness and Kristin P. Bennett},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.11190}
}
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision health. We develop a combined semantic and machine learning system that uses a health risk ontology and knowledge graph (KG) to dynamically discover risk factors and their… 

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References

SHOWING 1-10 OF 17 REFERENCES

Semantic Modeling of Cohort Descriptions in Research Studies 1

A prototype system rooted in Semantic Technologies to extract and model the baseline characteristics of cohorts utilized for cited research studies in the American Diabetes Association (ADA)’s Standards of Medical Care in Diabetes 20182 CPG is begun.

An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus

Despite difficulty in ascertaining causality, the potential for novel factors of large effect associated with T2D justify the use of EWAS to create hypotheses regarding the broad contribution of the environment to disease.

What Is a Knowledge Graph?

  • M. Kejriwal
  • Computer Science
    Domain-Specific Knowledge Graph Construction
  • 2019
While a knowledge graph seems to be a very simple way of representing information, it turns out to be quite powerful and is almost a lingua franca of sorts between humans and machines.

The Semantic Data Dictionary Approach to Data Annotation & Integration

The Semantic Data Dictionary (SDD) specification is presented, which allows for extension and integration of data from multiple domains using a common metadata standard and has developed a structure based on the Semanticscience Integrated Ontology (SIO) high-level, domain-agnostic conceptualization of scientific data, which is then annotated with more specific terminology from domain-relevant ontologies.

Data Analytics as Data : A Semantic Workflow Approach

This work examines the fundamental questions and capabilities that must be addressed to realize capturing and reasoning over workflows as well as interpreting and contextualizing their results, and focuses on capturing key components of complete workflow processes.

Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models

A discriminative model that simultaneously learns cadre assignment and target-prediction rules is introduced, and experimental results show that cadre methods have generalization that is competitive with linear and nonlinear regression models and can identify robust subpopulations.

What do we need to build explainable AI systems for the medical domain?

It is argued that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitates transparency and trust.

Association between blood lead and blood pressure: a population-based study in Brazilian adults

At low concentrations, BLL were positively associated with DBP and with the odds for hypertension in adults aged 40 or older, and it is important to enforce lead exposure monitoring and the enactment of regulatory laws to prevent lead contamination in urban settings.

The anatomy of a nanopublication

This document presents a model of nanopublications along with a Named Graph/RDF serialization of the model and discusses the importance of aggregating nanopublication and the role that the Concept Wiki plays in facilitating it.

Association of Blood Lead level with Elevated Blood Pressure in Hypertensive Patients.

It is indicated that a positive relationship exists between blood pressure and B-Pb levels and this may clarify the implication of Pb as leading risk factor for the cardiovascular diseases and hypertension.