Length of Stay Predictions: Improvements Through the Use of Automated Laboratory and Comorbidity Variables
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Summary
The inclusion of automated laboratory and comorbidity data improved LOS predictions in all models, underscoring the need for more widespread adoption of comprehensive electronic medical records.
- Type
- article
- Published
- 2010-08-01
- Cited by
- 89
- References
- 36
- OpenAlex
- https://openalex.org/W1967021867
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:22384136
Keywords
Interquartile range, Logistic regression, Comorbidity, Linear regression, Outlier
References
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- Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data?
- Comparing outcomes of care before and after implementation of the DRG-based prospective payment system.
- Regression modelling strategies for improved prognostic prediction.
- Fitting the distributions of length of stay by parametric models.
Cited by
- The Natural History of Code Status Changes and Its Implications on Inpatient Mortality in Younger and Older Hospitalized Patients
- LOSH Prediction using Data Mining
- Managing high‐risk surgical patients: modifiable co‐morbidities matter
- Mortality predictions on admission as a context for organizing care activities.
- An electronic order set for acute myocardial infarction is associated with improved patient outcomes through better adherence to clinical practice guidelines.
- An Electronic Simplified Acute Physiology Score-Based Risk Adjustment Score for Critical Illness in an Integrated Healthcare System*
- Inpatient palliative care consults and the probability of hospital readmission.
- A novel approach for predicting the length of hospital stay with DBSCAN and supervised classification algorithms
- Healthcare Data Mining: Predicting Hospital Length of Stay (PHLOS)
- Application of electronic medical record data for health outcomes research: a review of recent literature
- A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients
- Using clinical variables and drug prescription data to control for confounding in outcome comparisons between hospitals
- Factors associated with length of stay following an emergency medical admission.
- A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand intensive care adult patient data-base, 2008–2009
- ICU Admission Control: An Empirical Study of Capacity Allocation and Its Implication for Patient Outcomes
- Caregiver Status: A Simple Marker to Identify Patients at Risk for Longer Post-Operative Length of Stay, Rehospitalization or Death
- Multimorbidity in risk stratification tools to predict negative outcomes in adult population.
- Particle Swarm Optimization over Back Propagation Neural Network for Length of Stay Prediction
- Systematic Review of Risk Adjustment Models of Hospital Length of Stay (LOS)
- Predicting Hospital Length of Stay (PHLOS): A Multi-tiered Data Mining Approach
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