Why the study?
Existing hospital readmission prediction frameworks fail to address the challenge of utilizing large, high-dimensional, and continuously expanding datasets fundamental to readmission risk analysis.
Does an HPCC-based Naive Bayes classification framework improve the prediction of COPD hospital readmissions compared to existing models?
Does an HPCC-based Naive Bayes classification framework improve the prediction of COPD hospital readmissions compared to existing models?
An HPCC-based Naive Bayes framework significantly improves precision and recall for predicting COPD hospital readmissions while reducing computational time compared to standard models.
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May improve COPD readmission forecasting precision; leaves open prospective validation before clinical use.
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.
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Jain et al. (2019) studied this question.
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