Investigating the impact of age, gender, and comorbid conditions on the prolonged length of stay after endarterectomy-Reference-Cited by-同舟云学术

Investigating the impact of age, gender, and comorbid conditions on the prolonged length of stay after endarterectomy

Published:2021-08-13 Issue: Volume: Page:
ISSN:
Container-title:2021 International Symposium on Biomedical Engineering and Computational Biology
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Author:

De Lauri Chiara¹,Angela Trunfio Teresa²,Colella Ylenia¹,Lombardi Andrea³,Borrelli Anna³,Gargiulo Paolo⁴

Affiliation:

1. Department of Electrical Engineering and Information Technology, University of Study of Naples Federico II, Italy

2. Department of Advanced Biomedical Sciences, University Hospital of Naples Federico II, Italy

3. 'San Giovanni di Dio e Ruggi d'Aragona' University Hospital, Italy

4. Reykjavik University, Institute for Biomedical and Neural Engineering, Iceland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3502060.3503636

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4. T. A. Trunfio , A. Scala , A. D. Vecchia , A. Marra , and A. Borrelli , ‘Multiple Regression Model to Predict Length of Hospital Stay for Patients Undergoing Femur Fracture Surgery at “San Giovanni di Dio e Ruggi d'Aragona” University Hospital ’, in 8th European Medical and Biological Engineering Conference , Cham , 2021 , pp. 840– 847 , doi: 10.1007/978-3-030-64610-3_94. 10.1007/978-3-030-64610-3_94 T. A. Trunfio, A. Scala, A. D. Vecchia, A. Marra, and A. Borrelli, ‘Multiple Regression Model to Predict Length of Hospital Stay for Patients Undergoing Femur Fracture Surgery at “San Giovanni di Dio e Ruggi d'Aragona” University Hospital’, in 8th European Medical and Biological Engineering Conference, Cham, 2021, pp. 840–847, doi: 10.1007/978-3-030-64610-3_94.

5. A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

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