Due to various etiologies and pathogenesis of kidney diseases, an invasive procedure called renal biopsy may be needed to determine the specific type of kidney disease, its severity, and the best treatment for it. This study aims to detmine if a text understanding technology based on admission records can recommend such an invasive procedure objectively. To understand clinical documents from nephrology, a semi-automatic learning-based lexicon construction method based on CRF and Word2vec was used. We constructed a dictionary of symptom terms for the nephrology department from clinical document, and then extracted patients' symptoms and detected their negation from admission notes. Combined with the preliminary diagnosis given by the doctor, an eigenvector was produced and fed to a machine learning classifier. When compared to the gold standard marked by physicians, the final recommendation achieved 83.5% accuracy, 80.6% precision, 76.6% recall, and 78.6% f1-measure respectively.