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

Alam et al., MDPI AG, doi:10.20944/preprints202301.0341.v1 (Preprint)
Alam et al., Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based.., MDPI AG, doi:10.20944/preprints202301.0341.v1 (Preprint)
Jan 2023   Source   PDF  
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Deep learning model for the prediction of hospitalization time for COVID-19 based on 311 patients in Saudi Arabia. Authors report shorter hospitalization time for HCQ and favipiravir, but do not provide details.
This study includes HCQ and favipiravir.
Alam et al., 19 Jan 2023, Saudi Arabia, preprint, 7 authors, study period April 2020 - January 2021.
Contact: (corresponding author), mahmood}, nkaabia},,
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Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention Based Transformers and Association Mining: Covid-19 as Phenotype
Fakhare Alam, Obieda Ananbeh, Khalid Mahmood Malik, Abdulrahman Al Odayani, Ibrahim Bin Hussain, Naoufel Kaabia, Amal Al Aidaroos
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 F1score (.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.
Discharged Dataset CRFI for Discharged Patients' Category In the discharged patients' category, for LoS ≤ 1 week or Los ≤ 2 weeks, usage of anticoagulant, antibiotics and antiviral medications are important factors and indicates that timely intervention and dosage reduces LoS. For LoS ≤ 3 weeks, some of the important risk factors observed in the rules are elevated level of LDH (>225), D-Dimer (>500) and CRP (between 6 mg/L to 100 mg/L). Observed rules suggest that patients with abnormal values of these factors takes time to recover even if they provided with anticoagulant and antiviral medications. For LoS ≤ 4 weeks, the important risk factors observed are higher lymphocytes count (>1000 cells/µL), elevated PNN count (1000 -7000 mm3), comorbidities such as hypertension, higher respiratory rate (20-28 bps). The mining results on patients who stayed more than 4 weeks in the hospital shows less platelets count (<50000), abnormal X-ray, PTT>14.5, and higher PNN count. We found these patterns along with usage Conflicts of Interest: The authors declare no conflict of interest.
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