Cognitive ontologies for neuropsychiatric phenomics research.

Now that genome-wide association studies (GWAS) are dominating the landscape of genetic research on neuropsychiatric syndromes, investigators are being faced with complexity on an unprecedented scale. It is now clear that phenomics, the systematic study of phenotypes on a genome-wide scale, comprises a rate-limiting step on the road to genomic discovery. To gain traction on the myriad paths leading from genomic variation to syndromal manifestations, informatics strategies must be deployed to navigate increasingly broad domains of knowledge and help researchers find the most important signals. The success of the Gene Ontology project suggests the potential benefits of developing schemata to represent higher levels of phenotypic expression. Challenges in cognitive ontology development include the lack of formal definitions of key concepts and relations among entities, the inconsistent use of terminology across investigators and time, and the fact that relations among cognitive concepts are not likely to be well represented by simple hierarchical "tree" structures. Because cognitive concept labels are labile, there is a need to represent empirical findings at the cognitive test indicator level. This level of description has greater consistency, and benefits from operational definitions of its concepts and relations to quantitative data. Considering cognitive test indicators as the foundation of cognitive ontologies carries several implications, including the likely utility of cognitive task taxonomies. The concept of cognitive "test speciation" is introduced to mark the evolution of paradigms sufficiently unique that their results cannot be "mated" productively with others in meta-analysis. Several projects have been initiated to develop cognitive ontologies at the Consortium for Neuropsychiatric Phenomics (www.phenomics.ucla.edu), in the hope that these ultimately will enable more effective collaboration, and facilitate connections of information about cognitive phenotypes to other levels of biological knowledge. Several free web applications are available already to support examination and visualisation of cognitive concepts in the literature (PubGraph, PubAtlas, PubBrain) and to aid collaborative development of cognitive ontologies (Phenowiki and the Cognitive Atlas). It is hoped that these tools will help formalise inference about cognitive concepts in behavioural and neuroimaging studies, and facilitate discovery of the genetic bases of both healthy cognition and cognitive disorders.

IndexFinder: a method of extracting key concepts from clinical texts for indexing.

Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing.

Free-text medical document retrieval via phrase-based vector space model.

Many information retrieval systems are based on vector space model (VSM) that represents a document as a vector of index terms. Concepts have been proposed to replace word stems as the index terms to improve retrieval accuracy. However, past research revealed that such systems did not outperform the traditional stem-based systems. Incorporating conceptual similarity derived from knowledge sources should have the potential to improve retrieval accuracy. Yet the incompleteness of the knowledge source precludes significant improvement. To remedy this problem, we propose to represent documents using phrases. A phrase consists of multiple concepts and word stems. The similarity between two phrases is jointly determined by their conceptual similarity and their common word stems. The document similarity can in turn be derived from phrase similarities. Using OHSUMED as a test collection and UMLS as the knowledge source, our experiment results reveal that phrase-based VSM yields a 16% increase of retrieval accuracy compared to the stem-based model.

Modeling medical content for automated summarization.

Medical information is available from a variety of new online resources. Given the number and diversity of sources, methods must be found that will enable users to quickly assimilate and determine the content of a document. Summarization is one such tool that can help users to quickly determine the main points of a document. Previous methods to automatically summarize text documents typically do not attempt to infer or define the content of a document. Rather these systems rely on secondary features or clues that may point to content. This paper describes text summarization techniques that enable users to focus on the key content of a document. The techniques presented here analyze groups of similar documents in order to form a content model. The content model is used to select sentences forming the summary. The technique does not require additional knowledge sources; thus the method should be applicable to any set of text documents.