Misspellings, abbreviations and acronyms are very popular in clinical notes and can be an obstacle to high quality information extraction and classification. In addition, another important part of narrative reports is clinical scores and measurements as doctors infer a patient"s status by analyzing them. We introduce a knowledge discovery process to resolve unknown tokens and convert scores and measures into a standard layout so as to improve the quality of semantic processing of the corpus.

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... tem performance is evaluated before and after an automatic proof reading process by comparing the computed SNOMED-CT codes to the coding created originally by the clinical staff. The automatic coding of the texts increased the coded content by 15% after the automatic correction process and the number of unique codes increased by 4.7%. Accuracy of the automatic coding and annotations in the notes which have not been coded by the clinical staff is suggested by the system output.