Foundations of statistical natural language processing
Foundations of statistical natural language processing
Experiments in high-dimensional text categorization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Linear Least Squares Fit mapping method for information retrieval from natural language texts
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
A decision-tree-based symbolic rule induction system for text categorization
IBM Systems Journal
A machine learning approach for identifying subtypes of autism
Proceedings of the 1st ACM International Health Informatics Symposium
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This paper addresses a very specific problem that happens to be common in health science research. We present a machine learning based method for identifying patients diagnosed with congestive heart failure and other related conditions by automatically classifying clinical notes. This method relies on a Perceptron neural network classifier trained on comparable amounts of positive and negative samples of clinical notes previously categorized by human experts. The documents are represented as feature vectors where features are a mix of single words and concept mappings to MeSH and HICDA ontologies. The method is designed and implemented to support a particular epidemiological study but has broader implications for clinical research. In this paper, we describe the method and present experimental classification results based on classification accuracy and positive predictive value.