Deep Recurrent Survival Analysis

Ren, Kan; Qin, Jiarui; Zheng, Lei; Yang, Zhengyu; Zhang, Weinan; Qiu, Lin; Yu, Yong

doi:10.1609/aaai.v33i01.33014798

Cited by 78 publications

(58 citation statements)

References 31 publications

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“…Deep learning models with improved algorithms should be built and tested for more generic tasks. For example, a deep recurrent survival analysis which used LSTM cells as the building blocks has been proposed for survival analysis [96]. It will be interesting to test this model in cancer prognosis.…”

Section: Challenges In the Application Of Deep Learning In Cancer Promentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

269

161

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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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Section: Challenges In the Application Of Deep Learning In Cancer Promentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

269

161

View full text Add to dashboard Cite

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“…-C-index [14]: C-index measures the global pairwise ordering performance, and it is the most generally used evaluation metric in survival analysis [13,15].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…However, the disadvantage of this model is that the input for each cell is simple, and the input does not consider lower-level interactions. -Deep Recurrent Survival Analysis (DRSA) [15]: It is an auto-regressive model with LSTM. Each cell emits a hazard rate for each timestamp.…”

Section: Comparison With Baselinesmentioning

confidence: 99%

Revisit Prediction by Deep Survival Analysis

Kim

Song

Kim

et al. 2020

Lecture Notes in Computer Science

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In this paper, we introduce SurvRev, a next-generation revisit prediction model that can be tested directly in business. The SurvRev model offers many advantages. First, SurvRev can use partial observations which were considered as missing data and removed from previous regression frameworks. Using deep survival analysis, we could estimate the next customer arrival from unknown distribution. Second, SurvRev is an event-rate prediction model. It generates the predicted event rate of the next k days rather than directly predicting revisit interval and revisit intention. We demonstrated the superiority of the SurvRev model by comparing it with diverse baselines, such as the feature engineering model and state-of-the-art deep survival models.

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“…Bid Landscape Forecasting. Market price prediction has been widely studied in RTB [17,18,19]. One key challenge is that in the second price auctions, the second highest price, a.k.a the market price, is only shown to the winner and remains unknown to the others.…”

Section: Related Workmentioning

confidence: 99%

“…However, one aggregated distribution for all the bid requests fails to capture the divergence in the feature space. In [17], the authors proposed to adopt the recurrent network to model the sequential pattern in the feature space of the individual user and estimate the market price distribution for each bid request. However, the features may not only be limited to the sequential dependencies.…”

Section: Related Workmentioning

confidence: 99%

Know Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation

Cowen-Rivers²,

Wen

et al. 2019

2019 International Conference on Data Mining Workshops (ICDMW)

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Real-time bidding (RTB) is a popular method to sell online ad space inventory using real-time auctions to determine which advertiser gets to make the ad impression. Advertisers can take user information into account when making their bids and get more control over the process. The goal of an optimal bidding function is to maximise the overall effectiveness of the ad campaigns defined by the advertisers under a certain budget constraint. A straightforward solution would be to model the bidding function in an explicit form. However, such functional solutions lack generality in practice and are insensitive to the stochastic behaviour of other bidders in the environment. In this paper, we propose to formulate the online auctions into a general mean field multi-agent framework, in which the agents compete with each other and each agent's best response strategy depends on its opponents' actions. We firstly introduce a novel Deep Attentive Survival Analysis (DASA) model to estimate the opponent's action distribution on the ad impression level which outperforms state-of-the-art survival analysis. Furthermore, we introduce the DASA model as the opponent model into the Mean Field Deep Deterministic Policy Gradients (DDPG) algorithm for each agent to learn the optimal bidding strategy and converge to the mean field equilibrium. The experiments have shown that with the inference of the market, the market converges to the equilibrium faster while playing against both fixed strategy agents and dynamic learning agents.

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Deep Recurrent Survival Analysis

Cited by 78 publications

References 31 publications

The Application of Deep Learning in Cancer Prognosis Prediction

The Application of Deep Learning in Cancer Prognosis Prediction

Revisit Prediction by Deep Survival Analysis

Know Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation

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