Preoperative predictions of in-hospital mortality using electronic medical record data
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Summary
Using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery, with accuracy comparable to models trained on features that require clinical expertise.
- Type
- preprint
- Published
- 2018-05-25
- Cited by
- 3
- References
- 34
- Access
- Open access
- OpenAlex
- https://openalex.org/W2804210803
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:90620334
Keywords
Logistic regression, Random forest, Receiver operating characteristic, Decision tree, Medicine
References
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- Broadly Applicable Risk Stratification System for Predicting Duration of Hospitalization and Mortality
- Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.
- Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients
- Spectral Regularization Algorithms for Learning Large Incomplete Matrices
- SMOTE: Synthetic Minority Over-sampling Technique
- Validation of a combined comorbidity index.
- Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation
- Modeling Approaches and Algorithms for Advanced Computer Applications
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Cited by
- A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score
- A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients
- Predicting 30-Day In-Hospital Mortality in Surgical Patients: A Logistic Regression Model Using Comprehensive Perioperative Data
- Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review
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