J Am Med Inform Assoc - Finding falls in ambulatory care clinical documents using statistical text mining.


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JECTIVE: To determine how well statistical text mining (STM) models can identify falls within clinical text associated with an ambulatory encounter.MATERIALS AND METHODS: 2241 patients were selected with a fall-related ICD-9-CM E-code or matched injury diagnosis code while being treated as an outpatient at one of four sites within the Veterans Health Administration. All clinical documents within a 48-h window of the recorded E-code or injury diagnosis code for each patient were obtained (n=26 010; 611 distinct document titles) and annotated for falls. Logistic regression, support vector machine, and cost-sensitive support vector machine (SVM-cost) models were trained on a stratified sample of 70% of documents from one location (dataset Atrain) and then applied to the remaining unseen documents (datasets Atest-D).RESULTS: All three STM models obtained area under the receiver operating characteristic curve (AUC) scores above 0.950 on the four test datasets (Atest-D). The SVM-cost model obtained the highest AUC scores, ranging from 0.953 to 0.978. The SVM-cost model also achieved F-measure values ranging from 0.745 to 0.853, sensitivity from 0.890 to 0.931, and specificity from 0.877 to 0.944.DISCUSSION: The STM models performed well across a large heterogeneous collection of document titles. In addition, the models also generalized across other sites, including a traditionally bilingual site that had distinctly different grammatical patterns.CONCLUSIONS: The results of this study suggest STM-based models have the potential to improve surveillance of falls. Furthermore, the encouraging evidence shown here that STM is a robust technique for mining clinical documents bodes well for other surveillance-related topics.

Resumo Limpo

jectiv determin well statist text mine stm model can identifi fall within clinic text associ ambulatori encountermateri method patient select fallrel icdcm ecod match injuri diagnosi code treat outpati one four site within veteran health administr clinic document within h window record ecod injuri diagnosi code patient obtain n distinct document titl annot fall logist regress support vector machin costsensit support vector machin svmcost model train stratifi sampl document one locat dataset atrain appli remain unseen document dataset atestdresult three stm model obtain area receiv oper characterist curv auc score four test dataset atestd svmcost model obtain highest auc score rang svmcost model also achiev fmeasur valu rang sensit specif discuss stm model perform well across larg heterogen collect document titl addit model also general across site includ tradit bilingu site distinct differ grammat patternsconclus result studi suggest stmbase model potenti improv surveil fall furthermor encourag evid shown stm robust techniqu mine clinic document bode well surveillancerel topic

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