KnowMED Inc.

SODS: System On Demand Service

The System on Demand Service is a model driven application design framework for creating custom designed, dynamic, distributed, and collaborative data collection and integration platforms. SODS enables users of the system design dynamic and robust data collection instruments on demand, and deploy on mobile devices such as tablet PCs, PDAs or web enabled applications. SODS data collection instruments operate on both offline and online mode, support collaborative and team based work-flow processes, enable data validation and quality control at the point of entry, support complete field level audit trail, enable field level version control and rollback, advanced synchronization, and automated horizontal and vertical data integration. Dynamic form generation, strong security, and advanced navigation supported on mobile devices makes SODS a perfect solution for disaster preparedness, clinical research, field surveys and assessments (learn more...)

BLUE-Text: Biomedical Language Understanding and Extraction

BLUE-Text is a minimal-syntactic, semantic clinical text understanding technology. BLUE-Text is designed with automated integration and advanced analytic processing of clinical text in mind. BLUE-Text is resilient to bad quality (dirty) text, can handle multi-lingual entries, and is sensitive to the negation, and uncertainty in clinical utterances. BLUE-Text output is a formal OWL Ontology with complete mappings to a semantically unified biomedical vocabulary system that enables automated cross schema mappings between standard biomedical and clinical vocabularies (such as SNOMED-CT, ICD9/10, LOINC, MedDRA, etc.). Computer interpretable (formal) output generated by BLUE-Text enables automated integration of text and non-text data for computer reasoning, information retrieval and information integration (learn more...). 

X₂O Interrogator: Ontology Learning from Disparate XML Streams

X₂O is a semi-automated ontology learning algorithm designed to interrogate incoming streams of disparate and 'unfamiliar' XML messages and automatically construct a proto-ontology (T-Box) that can represent the heterogeneity of the schema and content contributed by different streams of XML. X₂O also detects change in schema of incoming XML stream and modifies the proto-ontology to accommodate the change. An example use-case for X₂O is automated and real-time integration of heterogeneous XML messages submitted by multiple disparate EMR systems to a biosurveillance system or data warehouse. X₂O employs advanced text processing and vocabulary encoding to normalize and integrate semi-structured and structured content at the same time it converts XML content to RDF/OWL representation (read more...) 

SUETS: Semantically Unified, Extensible Taxonomy Services

As depicted by it name, SUETS is a semantically unified repository of terminological knowledge for biomedical and clinical information. SUETS is developed based on Simple Knowledge Organization System (SKOS) and enables principled representation of multiple biomedical vocabularies as a unified and cohesive knowledgebase where terminological, taxonomic, human readable annotations, identification, and semantic relations between terms, and concepts are explicated and represented by a fine-grained model that enables ad-hoc extensions, version control, and collaboration. SUETS supports functionalities of a full blown modern taxonomy service, as it enables automated ontology learning, search, indexing, classification, and information retrieval from a multi-schema repository by semantic applications (learn more...) 

OPAL: Ontological Processing for AnaLytics

OPAL is an ontology driven, automated approach to designing a representative multi-dimensional database (cube) from dynamically changing RDF/Schema. Traditionally when underlying schema or information model of a databased enterprise changes, a new data warehouse should be constructed  and new analytic facilities needs to be implemented by human experts, a time consuming, intensive and expensive process. OPAL automates that process through ontological representation of meaningful dimensions and measures in the data and their relationships. An automated agent employs the ontology to automatically extract a representative warehouse schema and compile an analytic cube for the new warehouse as new information is persisted into the system. 

SI2P2: Semantic Information Integration for Personal and Public Health

SI2P2 is a powerful information retrieval tool for clinical and biological researchers to integrate multi-source, disparate and heterogeneous biological and clinical data, encode data automatically to biomedical and clinical vocabulary systems (through SUETS integration), and map it all to the openly and freely available knowledge sources on the Internet for augmented information retrieval. SI2P2 also enables applying clinical and biomedical ontologies as a modeling layer that can be used for patient classification, cohort finding, hypothesis generation and validation and discoveries. Current incarnation of SI2P2 is linked to the Linked Open Data framework and the SUETS platform and enables multi-disciplinary queries to the clinical and biological data from the perspective of information from the LOD.