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Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning
Chenxi Sun1,2, Shenda Hong3,4, Moxian Song1,2
1School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China.
Insights
This study introduces a deep learning model to predict coronavirus disease 2019 (COVID-19) patient outcomes and identify four distinct disease progression stages. The findings aid clinicians in better assessing and treating COVID-19 patients.
Area of Science:
- Artificial Intelligence
- Biomedical Informatics
- Computational Biology
Background:
- The COVID-19 pandemic necessitates early identification of disease progression for effective patient management.
- Existing research lacks clear definitions for COVID-19 stages and characterization of disease progression.
- Accurate prediction of patient outcomes is crucial for resource allocation and targeted treatment.
Purpose of the Study:
- To develop a temporal deep learning model for predicting COVID-19 patient outcomes.
- To identify and characterize distinct stages of COVID-19 disease progression.
- To assist clinicians in better assessing and treating COVID-19 patients.
Main Methods:
- A time-aware long short-term memory (T-LSTM) neural network was employed.
- An online open dataset of 485 COVID-19 patients from Wuhan was utilized for model training.
- The model considered patient biomarkers and irregular time intervals for dynamic relation grasping.
Main Results:
- The model achieved over 90% accuracy in outcome prediction by 12 days, with high accuracy at earlier time points (3-9 days).
- Four distinct stages of COVID-19 progression were identified, each associated with different patient statuses and mortality risks.
- Forty biomarkers were ranked, with reference values provided for each stage, and three major complications (myocardial, liver, and renal injury) were identified.
Conclusions:
- The developed T-LSTM model accurately predicts COVID-19 patient outcomes and identifies disease progression stages.
- The identified four-stage progression model and associated biomarkers can aid clinicians in patient assessment and treatment.
- This research contributes to a better understanding of COVID-19 dynamics and facilitates more effective medical resource utilization.
Background:
The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident.
Methods:
We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19.
Results:
Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient.
Conclusions:
To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.
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