NLP BASED CLINICAL DATA ANALYSIS FOR ASSESSING READMISSIONS OF PATIENTS WITH COPD

Priyanka V. Medhe , Dinesh D. Puri

Chronic Obstructive Pulmonary Disease, Natural Language Processing, Readmissions, Clinical Notes Pre-processing, Prediction Modelling

Natural language processing is a computer science field, which focuses on interactions between computers and human (natural) languages. The human languages are ambiguous unlike computer languages, which make its analysis and processing difficult. Most of the data present these days is in unstructured form such as Accident reports, Patient discharge summary, Criminal records etc, which makes it hard for computers to understand for further use and analysis. This unstructured text needs to be converted into structured form by clearly defining the sentence boundaries, word boundaries and context dependent character boundaries for further analysis. With the passage of recent federal legislation many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and Chronic Obstructive Pulmonary Disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, few have focused primarily on unstructured clinical notes. Here a framework is created for analyzing clinical notes and predicting readmissions of patients. Key steps include many algorithms within the field of data mining and machine learning, so a framework for component selection is created to select the best components. NLP is applied followed by some of processing techniques like, tokenization, stop words removal, stemming, pruning, semantic analysis, POS Tagger etc.

Volume 5 Issue 12, December-2017

Pages : 453-458