Using compund codes for automatic classification of clinical diagnoses

Studies in Health Technology and Informatics

Volumn 107, Issue , 2004, Pages 411-415

  Pakhomov, Serguei V ^a   Buntrock, James D ^a   Chute, Christopher G ^a  

^a MAYO CLINIC   (United States)

Author keywords

Automatic classification; clinical diagnoses; concept indexing

Indexed keywords

AUTOMATIC INDEXING; CODES (SYMBOLS); INFORMATION RETRIEVAL SYSTEMS; PROBLEM SOLVING; CLASSIFIERS;

AUTOMATIC CLASSIFICATION; BAYES CLASSIFIER; CLASSIFICATION SYSTEM; CLINICAL DIAGNOSIS; CONCEPT INDEXING; ERROR-PRONE PROCESS; LABOR INTENSIVE; PREDICTIVE MODELS; 'CURRENT; CONCEPT-BASED; LABOUR-INTENSIVE; MULTIPLE CODES; SIMPLE++;

DIAGNOSIS;

ALGORITHM; ARTICLE; BAYES THEOREM; CLASSIFICATION; CLINICAL MEDICINE; DIAGNOSIS; DOCUMENTATION; HUMAN; MEDICAL RECORD; BAYESIAN LEARNING; CLASSIFIER; CLEANING; CONFERENCE PAPER; CONTROLLED STUDY; PREDICTIVE MODEL; RANDOMIZED CONTROLLED TRIAL;

ABSTRACTING AND INDEXING; ALGORITHMS; BAYES THEOREM; CLINICAL MEDICINE; DIAGNOSIS; FORMS AND RECORDS CONTROL; HUMANS;

EID: 77955512211     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-949-3-411     Document Type: Article

References (12)

1. Yang, Y. and C. Chute. A linear least squares fit mapping method for information retrieval from natural language texts, in 14th International Conference on Computational Linguistics (COLING). 1992.
2. Lewis, D. Naive (Bayes) at forty: The independence assumption in information retrieval, in ECML-98. 1998.
3. Johnson, D., et al., A deci-sion-tree-based symbolic rule induction system for text categorization. IBM Systems Journal 2002. 41(3).
4. Nigam, K., J. Lafferty, and A. McCullum. Using Maximum Entropy for Text Classification, in IJCAI-99 Workshop on Machine Learning for Information Filtering. 1999.
5. Damerau, F., et al. Experiments in High Dimensional Text Categorization, in ACM SIGIR International Conference on Information Re-trieval. 2002.
6. Carlson, A.J., et al., SNoW User's Guide, Cognitive Computations Group-University of Illinois at Urbana/Champaign.
7. Yang, Y. An Example Based Mapping for text Categorization and Retrieval, in TOIS'94. 1994.
8. Yang, Y. Expert Network: Combining Word-based Matching and Human Experiences in Text Categorization and Retrieval, in SIGIR. 1994.
9. Pakhomov, S. Modeling Filled Pauses in Medical Dictations, in Association for Computational Linguistics (ACL'99). 1999.
10. Commission on Professional and Hospital Activities, Hospital Adaptation oflCDA. 2nd ed. Vol. 1. 1973, Ann Arbor, MI: Commission on Professional and Hospital Activities.
11. Domingos, P. and M. Pazzani, On the Optimality of the Simple Baysean Classifier under Zero-One Loss. Machine Learning, 1997(29): p. 103-130.
12. Manning, C. and S. H., Foundations of Statistical Natural Language Processing. 1999, Cambridge, MA: MIT Press.