An Automatic Electronic Nursing Records Analysis System Based on the Text Classification and Machine Learning

Enormous amount of unstructured electronic health record are invaluable for the medical research in finding the relationship between the patient´s disease and the final diagnosis. How to use computer automatically dig up these information has long been a hot spot. To get the relationship between the clinical outcomes and free text writing by nurse, we developed an automatic categorization system process natural language nursing record Based on vector space model. 210 cases of electronic nursing records, which were diagnosed as pancreatitis, were induced in this study. We filtered the restricted corpus for acute pancreatitis classification by information gain (information gain. IG), and construct a text classification system Based on Partial least squares discrimination algorithm (PLS-DA) and vector support machine (VSM). PLS loading value analysis found that there are 20 terms can be used to classify medical record text. Our innovative machine-learning algorithm effectively classified free texts of nurse care records associated with normal and acute pancreatitis diagnoses, after training on pre-classified test sets by PLS. This automatic identification technology focus in large-scale medical document may provide important clues to study the acute pancreatitis and other important common disease.