Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
The potential of latent semantic analysis for machine grading of clinical case summaries
Journal of Biomedical Informatics
Advances in Neural Information Processing Systems 5, [NIPS Conference]
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Geometry and Meaning
Human Problem Solving
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
IEEE Transactions on Signal Processing
Methodological Review: Empirical distributional semantics: Methods and biomedical applications
Journal of Biomedical Informatics
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Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested.