Contextual Sentiment Neural Network for Document Sentiment Analysis

Ito, Tomoki; Tsubouchi, Kota; Sakaji, Hiroki; Yamashita, Tatsuo; Izumi, Kiyoshi

doi:10.1007/s41019-020-00122-4

Cited by 36 publications

(18 citation statements)

References 12 publications

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“…The prediction accuracies at 3-, 6-, and 9-days prior are 98, 95 and 93%, respectively. Second, the DL method is the black-box models which are troubled by poor interpretability [46,47], but clinical settings prefer interpretable models. For example, finding the appropriate prediction-related biomarkers is important.…”

Section: Discussionmentioning

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

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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%

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

“…Second, the DL method is the black-box models which are troubled by poor interpretability [23,48], but clinical settings prefer interpretable models. For example, finding the appropriate prediction-related biomarkers is important.…”

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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“…Given these five datasets including the labeled data, we perform sentiment analysis for each aspect using a pretrained text model ELECTRA [9] which was recently proposed in ICLR 2020 with state-of-the-art performance. The results demonstrate that ELECTRA achieves better contextual sentiment analysis compared to the CSNN [10] model. The detailed hyper-parameter settings of ELECTRA are described in our support materials.…”

Section: Which Aspects Play Important Roles In Reviewmentioning

confidence: 96%

What have we learned from OpenReview?

Wang

Zhang

et al. 2022

World Wide Web

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Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 11,915 submissions and their 41,276 reviews from the OpenReview platform. We also collect these submissions' citation data from Google Scholar and their non-peer-reviewed versions from arXiv.org. By acquiring deep insights into these data, we have several interesting findings that could help understand the effectiveness of the publicaccessible double-blind peer review process. Our results can potentially help writing a paper, reviewing it, and deciding on its acceptance.

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Contextual Sentiment Neural Network for Document Sentiment Analysis

Cited by 36 publications

References 12 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

What have we learned from OpenReview?

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