U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19

Näppi, Janne J.; Uemura, Tomoki; Watari, Chinatsu; Hironaka, Tetsuo; Kamiya, Tohru; Yoshida, Hiroyuki

doi:10.1038/s41598-021-88591-z

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…Finally, Na ¨ppi, Uemura [111] created a U-survival and autonomous image-based survival modeling approach that integrates DL of chest CT images with the well-established survival analysis approach of an elastic-net Cox survival algorithm. In a study of 383 positive cases from two hospitals, U-survival's prognostic bootstrap prediction performance was higher than that of existing laboratory and image-based reference predictors for COVID-19 progression.…”

Section: Survival Analysis Methodsmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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Section: Survival Analysis Methodsmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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“…Bao and Wang (2020) introduced a multitask framework built on a SqueezeNet (Iandola et al, 2016) backbone for the segmentation of the lung and two classification tasks: the detection of COVID-19 and the estimation of three degrees of severity. Similar to our approach Näppi et al (2021) use the bottleneck features of a pretrained U-Net for the prediction of COVID-19 progression and mortality. However, they did not utilize a graph-based approach for the classification.…”

Section: Multitask Learning For Covid-19mentioning

confidence: 99%

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

Keicher¹,

Burwinkel²,

Bani-Harouni³

et al. 2021

Preprint

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During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. However, when dealing with a newly emerging disease such as COVID-19, the impact of patient-and disease-specific factors (e.g. body weight or known co-morbidities) on the immediate course of disease is by and large unknown. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients is often determined only by acute indicators such as vital signs (e.g. breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic graph-based approach combining both imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality. Additionally, the network segments chest CT images as an auxiliary task and extracts image features and radiomics for feature fusion with the available metadata. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention allow for increased understanding of the patient relationships within the population graph and provide insight into the network's decision-making process.

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