Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients’ LoS using a...

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Detalles Bibliográficos
Autores principales: Alam, Fakhare, Ananbeh, Obieda, Malik, Khalid Mahmood, Odayani, Abdulrahman Al, Hussain, Ibrahim Bin, Kaabia, Naoufel, Aidaroos, Amal Al, Saudagar, Abdul Khader Jilani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216944/
https://www.ncbi.nlm.nih.gov/pubmed/37238244
http://dx.doi.org/10.3390/diagnostics13101760

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216944/
https://www.ncbi.nlm.nih.gov/pubmed/37238244
http://dx.doi.org/10.3390/diagnostics13101760

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