• Mohammed Raziuddin
• R. Sateesh Kumar

With the entry of late government enactment numerous medicinal foundations are presently in charge of reaching target emergency clinic readmission rates. Endless sicknesses represent numerous medical clinic readmissions and Chronic Obstructive Pulmonary Disease has been as of late added to the rundown of ailments for which the United States government punishes emergency clinics bringing about over the top readmissions. Despite the fact that there have been endeavors to measurably foresee those most in threat of readmission, few have concentrated principally on unstructured clinical notes. We have proposed a structure which utilizes Natural Language Processing to break down clinical notes and foresee readmission. Numerous calculations inside the field of information mining and machine learning exist, so a system for segment determination is made to choose the best parts. Naïve Bayes using Chi-Squared feature determination offers an AUC of 0.690 while keeping up quick computational occasions.

Natural language processing, Medical information systems, Decision support systems, Data mining, Feature Extraction

"Hospital Readmission for Patients with COPD by analyzing EHR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.135-144, May-2019, Available :http://www.jetir.org/papers/JETIR1905K18.pdf

"Hospital Readmission for Patients with COPD by analyzing EHR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp135-144, May-2019, Available at : http://www.jetir.org/papers/JETIR1905K18.pdf