A predictive analytics approach to reducing avoidable hospital readmission
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Summary
A new readmission metric based on administrative data that can identify potentially avoidable readmissions from all other types of readmissions is developed that can directly incorporate patient's history of readmission and risk factors changes over time.
- Type
- preprint
- Published
- 2014-02-24
- Cited by
- 13
- References
- 56
- Access
- Open access
- OpenAlex
- https://openalex.org/W1794381015
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7602060
Keywords
Analytics, Predictive analytics, Computer science, Medicine, Data science
References
- Risk factors for early unplanned hospital readmission in the elderly
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- An Introduction to Markov Processes
- A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
- Regression and time series model selection in small samples
- A probabilistic model for predicting the probability of no-show in hospital appointments
- Exponential survival trees.
- Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure.
- Predicting Costs of Care Using a Pharmacy-Based Measure Risk Adjustment in a Veteran Population
- Associations Between Reduced Hospital Length of Stay and 30-Day Readmission Rate and Mortality: 14-Year Experience in 129 Veterans Affairs Hospitals
- Cluster-based fitting of phase-type distributions to empirical data
- Predicting 30-day all-cause hospital readmissions
- A predictive modeling approach to increasing the economic effectiveness of disease management programs
- Risk factors for nonelective hospital readmissions
Cited by
- An analytics appraoch to reducing hospital readmission
- An analytics approach to designing patient centered medical homes
- Predicting 30-day all-cause readmissions from hospital inpatient discharge data
- Scalable and accurate deep learning with electronic health records
- Optimal Timing of Physician Visits after Hospital Discharge to Reduce Readmission
- Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs
- A multivariate hierarchical Bayesian framework for healthcare predictions with application to medical home study in the Department of Veteran Affairs
- The Prevention of Depression: A Machine Learning Approach
- A Framework To Support Management Of HIV/AIDS Using K-Means And Random Forest Algorithm
- Detecting Depression in Arabic Texts Using AI Techniques
- On modeling nonhomogeneous Poisson process for stochastic simulation input analysis
- Estimating multiple step shifts in a gaussian process mean with an application to phase I control chart analysis
- Estimating Multiple Step Shifts in a Gaussian Process Mean with an Application to Phase I Control Chart Analysis
- Predictive Analytics in Healthcare: Reducing Readmission Rates
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