Automatic extraction of candidate nomenclature terms using the doublet method

NOBLE - Flexible concept recognition for large-scale biomedical natural language processing

Background

Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus.

NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system’s matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator.

Results

We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE’s performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems.

Conclusion

NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.

Creation of a retrospective searchable neuropathologic database from print archives at Toronto's University Health Network

University Health Network (UHN) Pathology, in its capacity of providing neuro-oncologic care, now utilizes a laboratory information system (LIS), which was instituted in September 2001. For the 75 years preceding the LIS, more than 50 000 pathology reports exist in paper format. High-throughput automated scanning of the paper archives was employed to add the most recent 30 years of paper records (30 000 neuropathology specimens) to the LIS. The searchable portable document format (PDF) files generated from the scans were filtered through a multi-tiered process driven by Java computer programs that selected relevant patient and diagnostic information. A second series of programs queried the neuropathologist-assigned diagnoses and successfully converted these to the standardized World Health Organization (WHO) format. This was achieved with a master list of key site and diagnostic terms, and prioritization rules that were determined on a trial and error basis. Categorization, verification, and consolidation were completed within 3 months and on a C$10 000 budget.

A system for sharing routine surgical pathology specimens across institutions: the Shared Pathology Informatics Network

This report presents an overview for pathologists of the development and potential applications of a novel Web enabled system allowing indexing and retrieval of pathology specimens across multiple institutions. The system was developed through the National Cancer Institute's Shared Pathology Informatics Network program with the goal of creating a prototype system to find existing pathology specimens derived from routine surgical and autopsy procedures ("paraffin blocks") that may be relevant to cancer research. To reach this goal, a number of challenges needed to be met. A central aspect was the development of an informatics system that supported Web-based searching while retaining local control of data. Additional aspects included the development of an eXtensible Markup Language schema, representation of tissue specimen annotation, methods for deidentifying pathology reports, tools for autocoding critical data from these reports using the Unified Medical Language System, and hierarchies of confidentiality and consent that met or exceeded federal requirements. The prototype system supported Web-based querying of millions of pathology reports from 6 participating institutions across the country in a matter of seconds to minutes and the ability of bona fide researchers to identify and potentially to request specific paraffin blocks from the participating institutions. With the addition of associated clinical and outcome information, this system could vastly expand the pool of annotated tissues available for cancer research as well as other diseases.

A self-scaling, distributed information architecture for public health, research, and clinical care

OBJECTIVE

This study sought to define a scalable architecture to support the National Health Information Network (NHIN). This architecture must concurrently support a wide range of public health, research, and clinical care activities.

STUDY DESIGN

The architecture fulfils five desiderata: (1) adopt a distributed approach to data storage to protect privacy, (2) enable strong institutional autonomy to engender participation, (3) provide oversight and transparency to ensure patient trust, (4) allow variable levels of access according to investigator needs and institutional policies, (5) define a self-scaling architecture that encourages voluntary regional collaborations that coalesce to form a nationwide network.

RESULTS

Our model has been validated by a large-scale, multi-institution study involving seven medical centers for cancer research. It is the basis of one of four open architectures developed under funding from the Office of the National Coordinator of Health Information Technology, fulfilling the biosurveillance use case defined by the American Health Information Community. The model supports broad applicability for regional and national clinical information exchanges.

CONCLUSIONS

This model shows the feasibility of an architecture wherein the requirements of care providers, investigators, and public health authorities are served by a distributed model that grants autonomy, protects privacy, and promotes participation.