Statistical analysis with missing data
Statistical analysis with missing data
Object-oriented development of a concept learning system for time-centered clinical data
Journal of Medical Systems
Data mining
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Computers and Biomedical Research
Information Processing and Management: an International Journal - Special issue on interactivity at the text retrieval conference (TREC)
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Towards the development of a conceptual distance metric for the UMLS
Journal of Biomedical Informatics
Journal of Biomedical Informatics - Special section: JAMA commentaries
Self-organizing map for cluster analysis of a breast cancer database
Artificial Intelligence in Medicine
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Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia-in which the ICD9-CM coding was found to be very poor-the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).