Predicting clinical outcomes and hospitalization stay of hospitalized COVID-19 patients by using Deep Learning methods
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Abstract
Predicting outcomes and other critical clinical events of hospitalized COVID-19 patients may provide a valuable asset to healthcare and a chance to improve patient outcomes. Here, we have analyzed over 10,000 hospitalized COVID-19 patients in the Houston Methodist Hospital at the Texas Medical Center from the beginning of pandemics till April of 2020. This work extends our previous study analyzing longitudinal symptomatics of the hospitalized patients by seeking to understand how standard patient clinical data, like demographics and comorbidities, together with symptom data from early hospitalization can be used to predict the clinical outcomes and hospitalization stay. Deep Learning (DL) classification and regression methods were applied to quantify patient record importance and to perform predictions. The results suggest that patient outcome can be predicted with up to 75% accuracy. However, the prediction of hospitalization stay was more complex indicating deeper optimization of features.
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SciScore for 10.1101/2022.01.28.22270040: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization Five-fold repeated random sub-sampling validation for model cross-validations. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Deep neural network and feature selection: DL models were implemented by using the Keras [4] and TensorFlow [5] framework executed in Python 3. TensorFlowsuggested: (tensorflow, RRID:SCR_016345)Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit …SciScore for 10.1101/2022.01.28.22270040: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization Five-fold repeated random sub-sampling validation for model cross-validations. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Deep neural network and feature selection: DL models were implemented by using the Keras [4] and TensorFlow [5] framework executed in Python 3. TensorFlowsuggested: (tensorflow, RRID:SCR_016345)Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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