A deep survival analysis method based on ranking

Jing, Bingzhong; Zhang, Tao; Wang, Zixian; Jin, Ying; Liu, Kuiyuan; Qiu, Wen‐Ze; Ke, Liangru; Sun, Ying; He, Caisheng; Hou, Dan; Tang, Lin‐Quan; Lv, Xing; Li, Chaofeng

doi:10.1016/j.artmed.2019.06.001

Cited by 55 publications

(50 citation statements)

References 21 publications

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“…Katzman et al [60]: https://github.com/jaredleekatzman/DeepSurv. Jing et al 2019 [61]: http:/github.com/sysucc-ailab/RankDeepSurv.…”

Section: Nn Models With No Feature Extractionmentioning

confidence: 99%

“…Their results showed that this model performed better than CoxPH models ( Table 1). Another neural network model, named RankDeepSurvival, has adapted the basic architecture of DeepSurv and increased the depth of the network to build 3-4 hidden layers' DNN to perform survival analysis in multiple datasets, including cancer datasets [61]. More importantly, they have updated the loss function by using the sum of mean squared error loss and a pairwise ranking loss based on ranking information on survival data [61].…”

Section: Nn Models With No Feature Extractionmentioning

confidence: 99%

“…Another neural network model, named RankDeepSurvival, has adapted the basic architecture of DeepSurv and increased the depth of the network to build 3-4 hidden layers' DNN to perform survival analysis in multiple datasets, including cancer datasets [61]. More importantly, they have updated the loss function by using the sum of mean squared error loss and a pairwise ranking loss based on ranking information on survival data [61]. They reported that RankDeepSurivival model outperformed CoxPH models and DeepSurv model in breast cancer datasets from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the German Breast Cancer Study Group (GBSG) ( Table 1).…”

Section: Nn Models With No Feature Extractionmentioning

confidence: 99%

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The Application of Deep Learning in Cancer Prognosis Prediction

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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“…Katzman et al [60]: https://github.com/jaredleekatzman/DeepSurv. Jing et al 2019 [61]: http:/github.com/sysucc-ailab/RankDeepSurv.…”

Section: Nn Models With No Feature Extractionmentioning

confidence: 99%

Section: Nn Models With No Feature Extractionmentioning

confidence: 99%

Section: Nn Models With No Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

235

142

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“…Additionally, prediction based on ECG monitoring was addressed in various aspects of health status, such as survival chances, mood and behavior, and health status. Survival, risk of cardiac death and other predictions related to cardiac problems were discussed in [213][214][215]. Alternatively, the prediction of mood changes and response to depression treatment were addressed in [39][40][41].…”

Section: Service-based Monitoring Systemsmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

179

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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“…Today, multivariable approaches tailored to time to event data such as survival random forests [3], support vector machines (SVMs) [4] or approaches building on the concept of boosting [5] [6] are routinely applied for this task. However, recent advances in deep neural networks promise to uncover complex dependencies in the data and thereby might allow for better predictions of survival, e.g., conditional on gene expression profiles [7] [8] [9] [10].…”

Section: General Introduc%onmentioning

confidence: 99%

Stratified neural networks in a time-to-event setting

Kuruc

Binder

Hess

2021

Preprint

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Deep neural networks are now frequently employed to predict survival conditional on omics-type biomarkers, e.g. by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data-sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.

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A deep survival analysis method based on ranking

Cited by 55 publications

References 21 publications

The Application of Deep Learning in Cancer Prognosis Prediction

The Application of Deep Learning in Cancer Prognosis Prediction

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Stratified neural networks in a time-to-event setting

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