HPCC based framework for COPD readmission risk analysis
Abstract Prevention of hospital readmissions has the potential of providing better quality of care to the patients and deliver significant cost savings. A review of existing readmission analysis frameworks based on data type, data size, disease conditions, algorithms and other features shows that ex...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-03-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-019-0189-0 |
| _version_ | 1818177011068174336 |
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| author | Piyush Jain Ankur Agarwal Ravi Behara Christopher Baechle |
| author_facet | Piyush Jain Ankur Agarwal Ravi Behara Christopher Baechle |
| author_sort | Piyush Jain |
| collection | DOAJ |
| description | Abstract Prevention of hospital readmissions has the potential of providing better quality of care to the patients and deliver significant cost savings. A review of existing readmission analysis frameworks based on data type, data size, disease conditions, algorithms and other features shows that existing frameworks do not address the issue of using large amounts of data that is fundamental to readmission prediction analysis. Available patient data for readmission risk analysis has high dimensionality and number of instances. Further, there is more new data produced everyday which can be used on a continuous basis to improve the prediction power of risk models. This study proposes a High Performance Computing Cluster based Big Data readmission risk analysis framework which uses Nave Bayes classification algorithm. The study shows that the over-all evaluation time using Big Data and a parallel computing platform can be significantly decreased, while maintaining model performance. |
| first_indexed | 2024-12-11T20:25:18Z |
| format | Article |
| id | doaj.art-5052110781374b4eaacd821a30ec9834 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-11T20:25:18Z |
| publishDate | 2019-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-5052110781374b4eaacd821a30ec98342022-12-22T00:51:58ZengSpringerOpenJournal of Big Data2196-11152019-03-016111310.1186/s40537-019-0189-0HPCC based framework for COPD readmission risk analysisPiyush Jain0Ankur Agarwal1Ravi Behara2Christopher Baechle3Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of Computer & Electrical Engineering and Computer Science, Florida Atlantic UniversityDepartment of IT and Operations Management, Florida Atlantic UniversityDepartment of Advanced Technology, Indian River State CollegeAbstract Prevention of hospital readmissions has the potential of providing better quality of care to the patients and deliver significant cost savings. A review of existing readmission analysis frameworks based on data type, data size, disease conditions, algorithms and other features shows that existing frameworks do not address the issue of using large amounts of data that is fundamental to readmission prediction analysis. Available patient data for readmission risk analysis has high dimensionality and number of instances. Further, there is more new data produced everyday which can be used on a continuous basis to improve the prediction power of risk models. This study proposes a High Performance Computing Cluster based Big Data readmission risk analysis framework which uses Nave Bayes classification algorithm. The study shows that the over-all evaluation time using Big Data and a parallel computing platform can be significantly decreased, while maintaining model performance.http://link.springer.com/article/10.1186/s40537-019-0189-0COPD readmissionPredictionNave BayesHPCCBig Data |
| spellingShingle | Piyush Jain Ankur Agarwal Ravi Behara Christopher Baechle HPCC based framework for COPD readmission risk analysis Journal of Big Data COPD readmission Prediction Nave Bayes HPCC Big Data |
| title | HPCC based framework for COPD readmission risk analysis |
| title_full | HPCC based framework for COPD readmission risk analysis |
| title_fullStr | HPCC based framework for COPD readmission risk analysis |
| title_full_unstemmed | HPCC based framework for COPD readmission risk analysis |
| title_short | HPCC based framework for COPD readmission risk analysis |
| title_sort | hpcc based framework for copd readmission risk analysis |
| topic | COPD readmission Prediction Nave Bayes HPCC Big Data |
| url | http://link.springer.com/article/10.1186/s40537-019-0189-0 |
| work_keys_str_mv | AT piyushjain hpccbasedframeworkforcopdreadmissionriskanalysis AT ankuragarwal hpccbasedframeworkforcopdreadmissionriskanalysis AT ravibehara hpccbasedframeworkforcopdreadmissionriskanalysis AT christopherbaechle hpccbasedframeworkforcopdreadmissionriskanalysis |