Simulating expert clinical comprehension: adapting latent semantic analysis to accurately extract clinical concepts from psychiatric narrative.
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.
Cohen
Trevor
T
Center for Decision Making and Cognition, Department of Biomedical Informatics, School of Computing and Informatics, Arizona State University, 425 N. 5th Street, Phoenix, Az 85004-2157, USA. trevor.cohen@asu.edu
Blatter
Brett
B
Patel
Vimla
V
D006801:Humans; D001523:Mental Disorders; D033262:Narration; D012660:Semantics