Automating classification of free-text electronic health records for epidemiological studies.

PURPOSE: Increasingly, patient information is stored in electronic medical records, which could be reused for research. Often these records comprise unstructured narrative data, which are cumbersome to analyze. The authors investigated whether text mining can make these data suitable for epidemiological studies and compared a concept recognition approach and a range of machine learning techniques that require a manually annotated training set. The authors show how this training set can be created with minimal effort by using a broad database query.
METHODS: The approaches were tested on two data sets: a publicly available set of English radiology reports for which International Classification of Diseases, Ninth Revision, Clinical Modification code needed to be assigned and a set of Dutch GP records that needed to be classified as either liver disorder cases or noncases. Performance was tested against a manually created gold standard.
RESULTS: The best overall performance was achieved by a combination of a manually created filter for removing negations and speculations and rule learning algorithms such as RIPPER, with high scores on both the radiology reports (positive predictive value = 0.88, sensitivity = 0.85, specificity = 1.00) and the GP records (positive predictive value = 0.89, sensitivity =0.91, specificity =0.76).
CONCLUSIONS: Although a training set still needs to be created manually, text mining can help reduce the amount of manual work needed to incorporate narrative data in an epidemiological study and will make the data extraction more reproducible. An advantage of machine learning is that it is able to pick up specific language use, such as abbreviations and synonyms used by physicians.