Anatomy is the structure of biological organisms. The term also denotes the scientific discipline devoted to the study of anatomical entities and the structural and developmental relations that obtain among these entities during the lifespan of an organism. Anatomical entities are the independent continuants of biomedical reality on which physiological and disease processes depend, and which, in response to etiological agents, can transform themselves into pathological entities. For these reasons, hard copy and in silico information resources in virtually all fields of biology and medicine, as a rule, make extensive reference to anatomical entities. Because of the lack of a generalizable, computable representation of anatomy, developers of computable terminologies and ontologies in clinical medicine and biomedical research represented anatomy from their own more or less divergent viewpoints. The resulting heterogeneity presents a formidable impediment to correlating human anatomy not only across computational resources but also with the anatomy of model organisms used in biomedical experimentation. The Foundational Model of Anatomy (FMA) ontology is being developed to fill the need for a generalizable anatomy ontology, which can be used and adapted by any computer-based application that requires anatomical information. Moreover it is evolving into a standard reference for divergent views of anatomy and a template for representing the anatomy of animals. A distinction is made between the FMA ontology as a theory of anatomy and the implementation of this theory as the FMA artifact. In either sense of the term, the FMA is a spatial-structural ontology of the entities and relations which together form the phenotypic structure of the human organism at all biologically salient levels of granularity. Making use of explicit ontological principles and sound methods, it is designed to be understandable by human beings and navigable by computers. The FMA’s ontological structure provides for machine-based inference, enabling powerful computational tools of the future to reason with biomedical data.

The Foundational Model of Anatomy Ontology

Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation.

Using online social networks to track a pandemic: A systematic review.

An unsupervised machine learning model for discovering latent infectious diseases using social media data

Values, challenges and future directions of big data analytics in healthcare: A systematic review.

Visualization and analytics tools for infectious disease epidemiology

When different groups create models or ontologies of the same knowledge domain, this creates challenges for knowledge sharing. To identify these challenges, we compare cellular structure as modeled by the Foundational Model of Anatomy (FMA), the Gene Ontology (GO), and the Cell Component Ontology (CCO). These ontologies all model the physical anatomy of a cell, and we expected them to be similar in scope. However, we discovered that the actual differences among them are substantial. These differences represent variations based on theory-driven vs. emergent construction, as well as differences in how small application ontologies like the CCO are created from reference ontologies. In this paper, we provide a description and analysis of these differences. By studying differences in language, granularity, breadth of coverage, and model organization, we hope to gain a better understanding of how to map between related ontologies.

Differences among cell-structure ontologies: FMA, GO, & CCO.

This article provides a review of selected past and present games for health. This will highlight some existing games for health and examine the relationship between health content and game technology. Because of this relationship, new technology trends may suggest future approaches to the application of videogame-based health interventions.

Games for Health: Past, Present, and Future?

Investigation of infectious disease cases requires support from protocols, distributed and cooperative work, and information systems. We used form and protocol reviews together with qualitative research to study public health work practices, data requirements, and information needs. We found that the forms and protocols did not fully align, and that current information systems were limited in their ability support data analysis. We propose that better alignment between protocols, work practices, and system functionality such as integrated information visualization will improve the disease investigation process.

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Alan P. Au

Papers

Visualization and analytics tools for infectious disease epidemiology

Differences among cell-structure ontologies: FMA, GO, & CCO.

Games for Health: Past, Present, and Future?

Assessing the Work Practices and Information Needs of Disease Investigators

Differences among cell-structure ontologies