PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype
Version 1
: Received: 15 January 2023 / Approved: 19 January 2023 / Online: 19 January 2023 (02:00:16 CET)
How to cite:
Alam, F.; Ananbeh, O.; Malik, K.M.; Odayani, A.A.; Hussain, I.B.; Kaabia, N.; Aidaroos, A.A. Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype. Preprints2023, 2023010341. https://doi.org/10.20944/preprints202301.0341.v1
Alam, F.; Ananbeh, O.; Malik, K.M.; Odayani, A.A.; Hussain, I.B.; Kaabia, N.; Aidaroos, A.A. Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype. Preprints 2023, 2023010341. https://doi.org/10.20944/preprints202301.0341.v1
Alam, F.; Ananbeh, O.; Malik, K.M.; Odayani, A.A.; Hussain, I.B.; Kaabia, N.; Aidaroos, A.A. Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype. Preprints2023, 2023010341. https://doi.org/10.20944/preprints202301.0341.v1
APA Style
Alam, F., Ananbeh, O., Malik, K.M., Odayani, A.A., Hussain, I.B., Kaabia, N., & Aidaroos, A.A. (2023). Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype. Preprints. https://doi.org/10.20944/preprints202301.0341.v1
Chicago/Turabian Style
Alam, F., Naoufel Kaabia and Amal Al Aidaroos. 2023 "Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype" Preprints. https://doi.org/10.20944/preprints202301.0341.v1
Abstract
Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.
Keywords
Deep Learning; COVID-19; Clinical Informatics; Machine Learning; Transformer; Association Mining
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.