Deep Learning for Improved Risk Prediction in Surgical Outcomes

Jalali, Ali; Lonsdale, Hannah; Do, Nhue; Peck, Jacquelin; Gupta, Monesha; Kutty, Shelby; Ghazarian, Sharon R.; Jacobs, Jeffrey P.; Rehman, Mohamed A.; Ahumada, Luis M.

doi:10.1038/s41598-020-62971-3

Cited by 57 publications

(45 citation statements)

References 48 publications

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“…In recent years, deep learning (DL) technology has been widely used because of its superior performance in various medical applications [28,29], such as medical image recognition [39] and medication recommendations [40]. In the past year, the spread of COVID-19 has had a peripheral effect on the global economy and health.…”

Section: Discussionmentioning

confidence: 99%

“…However, in terms of prediction accuracy, nondeep learning is not as good as deep learning methods. Deep learning methods can train the parameters with complex nonlinearity to learn the data structures and have achieved state-of-the-art in many medical prediction tasks [28][29][30]. Thus, many current works apply deep learning methods for COVID-19 prediction tasks [17,[19][20][21][22][23][24][25][26].…”

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confidence: 99%

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Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Sun

Hong

Song

et al. 2021

BMC Med Inform Decis Mak

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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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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Sun

Hong

Song

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning (DL) technology has been widely used because of its superior performance in various medical applications [19,20], such as medical image recognition [21] and medication recommendations [22]. In the past year, the spread of COVID-19 has had a peripheral effect on the global economy and health.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 Disease Progression and Patient Outcomes based on Temporal Deep Learning

Sun

Hong

Song

et al. 2020

Preprint

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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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“…Moreover, Figure 2(c) shows the dentists' ratings on the attention-based localization for soft deposits. While the attention-based method has been widely applied to interpret CNNs 30,31 , it lacks formal evaluations of location-indicating accuracy for the dental diagnosis purpose. According to the experiment, our model achieved scores with a median of 3.00, mean of 2.81, and std of 1.02, on a scale from 1 to 5.…”

Section: Evaluation Metrics and Statistical Analysismentioning

confidence: 99%

A Deep Learning Approach to Automatic Gingivitis Screening based Onclassification and Localization in RGB Photos

Liang

Zhang

et al. 2020

Preprint

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Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. To increase the availability, this study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile the box-wise localization sensitivity for gingivitis and dental calculus were 66.57% and 45.61%. Moreover, according to a consistency evaluation with three board-certificated dentists, the model achieved a median score of 3.0/5.0 for reasoning locations of soft deposits without any spatial supervision. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. The results show the potential of deep learning for enabling cost-effective screening of dental diseases among large populations.

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Deep Learning for Improved Risk Prediction in Surgical Outcomes

Cited by 57 publications

References 48 publications

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

Predicting COVID-19 Disease Progression and Patient Outcomes based on Temporal Deep Learning

A Deep Learning Approach to Automatic Gingivitis Screening based Onclassification and Localization in RGB Photos

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