Interpretable and robust hospital readmission predictions from Electronic Health Records-Reference-Cited by-同舟云学术

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Interpretable and robust hospital readmission predictions from Electronic Health Records

Published:2023-12-15 Issue: Volume:32 Page:3679-3687
ISSN:
Container-title:2023 IEEE International Conference on Big Data (BigData)
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Author:

Calero-Díaz Hugo¹,Hamad Rebeen Ali¹,Atallah Christian¹,Casement John²,Canoy Dexter³,Reynolds Nick J³,Barnes Michael⁴,Missier Paolo¹

Affiliation:

1. Newcastle University,School of Computing,Newcastle upon Tyne,UK

2. Newcastle University,Bioinformatics Support Unit,Newcastle upon Tyne,UK

3. Newcastle University,Faculty of Medical Sciences,Newcastle upon Tyne,UK

4. Queen Mary’s University of London,Centre for Translational Bioinformatics,London,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385234/10386078/10386820.pdf?arnumber=10386820

Reference52 articles.

1. Predictive models for hospital readmission risk: A systematic review of methods

2. A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model

3. Neural and statistical predictors for time to readmission in emergency departments: A case study

4. Extraction of actionable knowledge to reduce hospital readmissions through patients personalization

5. Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

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