Biomedical subjects

Abdul V Roudsari

Publications and source records attributed to Abdul V Roudsari.

4 recordsLinked to original sources

PubMed2002

A simple method for serving Web hypermaps with dynamic database drill-down.

BACKGROUND: HealthCyberMap http://healthcybermap.semanticweb.org aims at mapping parts of health information cyberspace in novel ways to deliver a semantically superior user experience. This is achieved through "intelligent" categorisation and interactive hypermedia visualisation of health resources using metadata, clinical codes and GIS. HealthCyberMap is an ArcView 3.1 project. WebView, the Internet extension to ArcView, publishes HealthCyberMap ArcView Views as Web client-side imagemaps. The basic WebView set-up does not support any GIS database connection, and published Web maps become disconnected from the original project. A dedicated Internet map server would be the best way to serve HealthCyberMap database-driven interactive Web maps, but is an expensive and complex solution to acquire, run and maintain. This paper describes HealthCyberMap simple, low-cost method for "patching" WebView to serve hypermaps with dynamic database drill-down functionality on the Web. RESULTS: The proposed solution is currently used for publishing HealthCyberMap GIS-generated navigational information maps on the Web while maintaining their links with the underlying resource metadata base. CONCLUSION: The authors believe their map serving approach as adopted in HealthCyberMap has been very successful, especially in cases when only map attribute data change without a corresponding effect on map appearance. It should be also possible to use the same solution to publish other interactive GIS-driven maps on the Web, e.g., maps of real world health problems.

Journal Article↗

PubMed2002

HealthCyberMap: a semantic visual browser of medical Internet resources based on clinical codes and the human body metaphor.

OBJECTIVES: HealthCyberMap (HCM-http://healthcybermap.semanticweb.org) is a web-based service for healthcare professionals and librarians, patients and the public in general that aims at mapping parts of the health information resources in cyberspace in novel ways to improve their retrieval and navigation. METHODS AND SERVICE DESCRIPTION: HCM adopts a clinical metadata framework built upon a clinical coding ontology for the semantic indexing, classification and browsing of Internet health information resources. A resource metadata base holds information about selected resources. HCM then uses GIS (Geographic Information Systems) spatialization methods to generate interactive navigational cybermaps from the metadata base. These visual cybermaps are based on familiar medical metaphors. CONCLUSIONS: HCM cybermaps can be considered as semantically spatialized, ontology-based browsing views of the underlying resource metadata base. Using a clinical coding scheme as a metric for spatialization ('semantic distance') is unique to HCM and is very much suited for the semantic categorization and navigation of Internet health information resources. Clinical codes ensure reliable and unambiguous topical indexing of these resources. HCM also introduces a useful form of cyberspatial analysis for the detection of topical coverage gaps in the resource metadata base using choropleth (shaded) maps of human body systems.

Abstracting and Indexing↗

PubMed2002

Towards a semantic medical Web: HealthCyberMap's tool for building an RDF metadata base of health information resources based on the Qualified Dublin Core Metadata Set.

BACKGROUND: HealthCyberMap (http://healthcybermap.semanticweb.org/) aims at mapping Internet health information resources in novel ways for enhanced retrieval and navigation. This is achieved by collecting appropriate resource metadata in an unambiguous form that preserves semantics. MATERIAL/METHODS: We modelled a qualified Dublin Core (DC) metadata set ontology with extra elements for resource quality and geographical provenance in Prot g -2000. A metadata collection form helps acquiring resource instance data within Prot g . The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Prot g -2000 plug-in. The project is saved in RDFS/RDF. RESULTS: The ontology and associated form serve as a free tool for building and maintaining an RDF medical resource metadata base. The UMLS tab enables browsing and searching for concepts that best describe a resource, and importing them to DC subject fields. The resultant metadata base can be used with a search and inference engine, and have textual and/or visual navigation interface(s) applied to it, to ultimately build a medical Semantic Web portal. Different ways of exploiting Prot g -2000 RDF output are discussed. CONCLUSIONS: By making the context and semantics of resources, not merely their raw text and formatting, amenable to computer 'understanding,' we can build a Semantic Web that is more useful to humans than the current Web. This requires proper use of metadata and ontologies. Clinical codes can reliably describe the subjects of medical resources, establish the semantic relationships (as defined by underlying coding scheme) between related resources, and automate their topical categorisation.

Humans↗

PubMedDate not supplied

Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients.

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.

Artificial Intelligence↗