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Prognostic COPD healthcare management system
Title
Prognostic COPD healthcare management system.
Creator
Jain, Piyush, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts....
Show moreHospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this project, we will focus on COPD (Chronic Obstructive Pulmonary Disease) which is one of the leading causes of disability and mortality worldwide. This project will design and develop a prognostic COPD healthcare management system which is a sustainable clinical decision-support system to reduce the number of readmissions by identifying those patients who need preventive interventions to reduce the probability of being readmitted. Based on patient’s clinical records and discharge summary, our system would be able to determine the readmission risk profile of patients treated for COPD. Suitable interventions could then be initiated with the objective of providing quality and timely care that helps prevent avoidable readmission.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004125, http://purl.flvc.org/fau/fd/FA00004125
Subject Headings
Integrated delivery of health care, Lungs -- Diseases, Obstructive -- Treatment, Medical care -- Quality control
Format
Document (PDF)
HPCC based Platform for COPD Readmission Risk Analysis with implementation of Dimensionality reduction and balancing techniques
Title
HPCC based Platform for COPD Readmission Risk Analysis with implementation of Dimensionality reduction and balancing techniques.
Creator
Jain, Piyush, Agarwal, Ankur, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts....
Show moreHospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this study, we will be proposing a framework on how the readmission analysis and other healthcare models could be deployed in real world and a Machine learning based solution which uses patients discharge summaries as a dataset to train and test the machine learning model created. Current systems does not take into consideration one of the very important aspect of solving readmission problem by taking Big data into consideration. This study also takes into consideration Big data aspect of solutions which can be deployed in the field for real world use. We have used HPCC compute platform which provides distributed parallel programming platform to create, run and manage applications which involves large amount of data. We have also proposed some feature engineering and data balancing techniques which have shown to greatly enhance the machine learning model performance. This was achieved by reducing the dimensionality in the data and fixing the imbalance in the dataset. The system presented in this study provides a real world machine learning based predictive modeling for reducing readmissions which could be templatized for other diseases.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013560
Subject Headings
Machine learning, Big data, Patient Readmission, Hospitals--Admission and discharge--Data processing, High performance computing
Format
Document (PDF)