Cited by 1,995Machine Learning in HealthcareStatistical Methods and InferenceRecommender Systems and Techniques

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DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

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Katzman, Jared L., Shaham, Uri, Cloninger, Alexander, Bates, Jonathan, Jiang, Tingting, Kluger, Yuval·2018·BMC Medical Research Methodology

DOI 10.1186/s12874-018-0482-1PMC PMC5828433

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Abstract

BACKGROUND: Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. METHODS: We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. RESULTS: We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. CONCLUSIONS: The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

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Fig. 1 Diagram of DeepSurv. DeepSurv is a configurable feed-forward deep neural network. The input to the network is the baseline data x. The network propagates the inputs through a number of hidden layers with weights θ. The hidden layers consist of fully-connected nonlinear activation functions followed by dropout. The final layer is a single node which performs a linear combination of the hidden features. The output of the network is taken as the predicted log-risk function ĥθ(x). The hyper-parameters of the network (e.g. number of hidden layers, number of nodes in each layer, dropout probability, etc.) were determined from a random hyper-parameter search and are detailed in Table 3CC BY · PubMed Central

Fig. 2 Simulated Linear Experimental Log-Risk Surfaces. Predicted log-risk surfaces and errors for the simulated survival data with linear log-risk function with respect to a patient’s covariates x0 and x1. a The true log-risk h(x)=x0+2x1 for each patient. b The predicted log-risk surface of ĥβ(x) from the linear CPH model parameterized by β. c The output of DeepSurv ĥθ(x) predicts a patient’s log-risk. d The absolute error between true log-risk h(x) and CPH’s predicted log-risk ĥβ(x). e The absolute error between true log-risk h(x) and DeepSurv’s predicted log-risk ĥθ(x)CC BY · PubMed Central

Fig. 3 Simulated Nonlinear Experimental Log-Risk Surfaces. Log-risk surfaces of the nonlinear test set with respect to patient’s covariates x0 and x1. a The calculated true log-risk h(x) (Eq. 9) for each patient. b The predicted log-risk surface of ĥβ(x) from the linear CPH model parameterized on β. The linear CPH predicts a constant log-risk. c The output of DeepSurv ĥθ(x) is the estimated log-risk functionCC BY · PubMed Central

Fig. 4 Simulated Treatment Log-Risk Surface. Treatment Log-Risk Surfaces as a function of a patient’s relevant covariates x0 and x1. a The true log-risk h1(x) if all patients in the test set were given treatment τ=1. We then manually set all treatment groups to either τ=0 or τ=1. b The predicted log-risk ĥ0(x) for patients with treatment group τ=0. c The network’s predicted log-risk ĥ1(x) for patients in treatment group τ=1CC BY · PubMed Central

Fig. 5 Simulated Treatment Survival Curves. Kaplan-Meier estimated survival curves with confidence intervals (α=.05) for the patients who were given the treatment concordant with a method’s recommended treatment (Recommendation) and the subset of patients who were not (Anti-Recommendation). We perform a log-rank test to validate the significance between each set of survival curves. a Effect of DeepSurv’s Treatment Recommendations (Simulated Data), b Effect of RSF’s Treatment Recommendations (Simulated Data)CC BY · PubMed Central

Fig. 6 Rotterdam & German Breast Cancer Study Group (GBSG) Survival Curves. Kaplan-Meier estimated survival curves with confidence intervals (α=.05) for the patients who were given the treatment concordant with a method’s recommended treatment (Recommendation) and the subset of patients who were not (Anti-Recommendation). We perform a log-rank test to validate the significance between each set of survival curves. a Effect of DeepSurv’s Treatment Recommendations (GBSG), b Effect of RSF’s Treatment Recommendations (GBSG)CC BY · PubMed Central

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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
“more than one hidden layer) and apply modern techniques such as weight decay regularization, Rectified Linear Units (ReLU) [17] with batch normalization [18], Scaled Exponential Linear Units (SELU) [19], dropout [20], gradient descent optimization algorithms (Stochastic Gradient Descent and Adaptive Moment Estimation…”
Method
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Untitled work
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297 · cited 1×
“iables in outcomes such as death or cancer recurrence and subsequently inform patients of their treatment options [1, 2, 3, 4]. One standard survival model is the Cox proportional hazards model (CPH) [5]. The CPH arXiv:1606.00931v3 [stat.ML] 9 Aug 2017 is a semiparametric model that calculates the effects of observed…”
Method
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Untitled work
(2010) · International Conference on Machine Learning · Cited by 13,995
Modeling Survival Data: Extending the Cox Model
Therneau, T. M., & Grambsch, P. M. (2000) · Statistics for Biology and Health · doi:10.1007/978-1-4757-3294-8 · Cited by 8,674
Untitled work
(2012) · Cited by 8,009
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097 · cited 1×
“First, we simulate clinical treatment data by including an additional covariate to the simulated data from Section 4.1.2.”
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Islam, N., Reuben, J. S., Dale, J. L., Coates, J. W., Sapiah, K., Markson, F. R., Jordan, C. T., & Smith, C. A. (2024) · JMIR Cancer · doi:10.2196/54740 · Cited by 8
Conditional probabilistic diffusion model driven synthetic radiogenomic applications in breast cancer
Chen, L., Huang, Z. H., Sun, Y., Domaratzki, M., Liu, Q., & Hu, P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012490 · Cited by 8
Exploiting common patterns in diverse cancer types via multi-task learning
Wu, B., Arriagada, S. O., Hsu, T.-C., Lin, T.-W., & Lin, C. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00700-z · Cited by 8
“Huang et al. ’ s study 33 compared Cox-based models like Cox-nnet 34 , DeepSurv, and AECOX 33 across twelve cancer types.”
Identification of gene regulatory networks associated with breast cancer patient survival using an interpretable deep neural network model
Wang, X., Sarangi, V., Wickland, D. P., Li, S., Chen, D., Thompson, E. A., Jenkinson, G., & Asmann, Y. W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125632 · Cited by 8
Deep learning for regression analysis of interval-censored data
Du, M., Wu, Q., Tong, X., & Zhao, X. (2024) · Electronic Journal of Statistics · doi:10.1214/24-ejs2298 · Cited by 8
TorchSurv: A Lightweight Package for Deep Survival Analysis
Monod, M., Krusche, P., Cao, Q., Sahiner, B., Petrick, N., Ohlssen, D. I., & Coroller, T. P. (2024) · The Journal of Open Source Software · doi:10.21105/joss.07341 · Cited by 8
Multimodal Fusion Framework Based on Low-Rank Interaction for Tumor Prognostic Prediction
An, Y., Lan, R., Lin, H., Wang, Y., Deng, H., Liu, Z., Wang, L., Liu, Z., Lu, C. Y., Yang, H., et al. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3578334 · Cited by 8
“Katzman et al. [25] introduced high-throughput transcriptomics data from RNA-Seq and established a Cox-net model.”
A hybrid deep survival model for failure modeling of water distribution networks coupling physical survival and data reconstruction
Wang, C., Zhou, H., Lin, S., Weng, X., Shao, Y., & Yu, T. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111401 · Cited by 8
Optimizing retinal images based carotid atherosclerosis prediction with explainable foundation models
Lee, H., Kim, J., Kwak, S., Rehman, A., Park, S. M., & Chang, J. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01957-9 · Cited by 8
“However, employing DeepSurv, which leverages deep learning to enhance Cox PH modeling, could improve prediction performance.”
Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network
Li, W., Fu, S., Gao, X., Lü, Z., Jin, R., Qin, N., Zhang, X., Wu, Y., Li, W., & Wang, J. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1141408 · Cited by 8
Survival Prediction Using Transformer-Based Categorical Feature Representation in the Treatment of Diffuse Large B-Cell Lymphoma
Pant, S., Kang, S., Lee, M., Phuc, P.-S., Yang, H.-J., & Yang, D. (2023) · Healthcare · doi:10.3390/healthcare11081171 · Cited by 8
Retinopathy prediction in type 2 diabetes: Time-varying Cox proportional hazards and machine learning models
Looareesuwan, P., Boonmanunt, S. W., Siriyotha, S., Lukkunaprasit, T., Thammasudjarit, R., Pattanaprateep, O., Nimitphong, H., Reutrakul, S., Attia, J., McKay, G. J., et al. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101285 · Cited by 7
Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma
Wan, G., Leung, B. W., DeSimone, M. S., Nguyen, N., Rajeh, A. A., Collier, M. R., Rashdan, H., Roster, K., Zhou, X., Moseley, C., et al. (2023) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2023.08.105 · Cited by 8
Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients
Starke, S., Zwanenburg, A., Leger, K., Lohaus, F., Linge, A., Kalinauskaitė, G., Tinhofer, I., Guberina, N., Guberina, M., Balermpas, P., et al. (2023) · Cancers · doi:10.3390/cancers15194897 · Cited by 8
SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients
Yang, P., Qiu, H., Yang, X., Wang, L., & Wang, X. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108159 · Cited by 8
Multi-Modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation
Zhong, Z., Li, J., Sollee, J., Collins, S., Bai, H. X., Zhang, P. J., Healey, T. T., Atalay, M. K., Gao, X., & Jiao, Z. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3417849 · Cited by 10
“These models utilize image encoders and text encoders in MRANet for feature extraction; their respective survival predictors are RSF [21], CPH model [22], and Vanilla [23].”
tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction
Zeng, L., Zhang, J., Chen, W., & Ding, Y. (2024) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlae051 · Cited by 8
Deep contrastive learning for predicting cancer prognosis using gene expression values
Sun, A., Franzmann, E. J., Chen, Z., & Cai, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae544 · Cited by 9
Prognostic importance of the Scottish inflammatory prognostic score in patients with hepatocellular carcinoma after hepatectomy: a retrospective cohort study
Shen, S., Qiu, X., Yang, C., Li, J., Peng, Y., Wen, Z., Luo, H., & Xiang, B. (2024) · BMC Cancer · doi:10.1186/s12885-024-13174-w · Cited by 8
“DeepSurv, a deep learning model specialized in analyzing survival time data, was utilized due to its enhanced adaptability to intricate data relationships, surpassing traditional CPH model [13].”
TSDLPP: A Novel Two-Stage Deep Learning Framework For Prognosis Prediction Based on Whole Slide Histopathological Images
Liu, Y., Li, A., Liu, J., Meng, G., & Wang, M. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3080295 · Cited by 10
Deep survival analysis with longitudinal X-rays for COVID-19
Shu, M., Bowen, R. S., Herrmann, C., Qi, G., Santacatterina, M., & Ramin Zabih, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00401 · Cited by 8
Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
Kim, B., Jang, Y. J., Cho, H. R., Kim, S. Y., Jeong, J. E., Shim, M. K., & Kim, M. G. (2021) · Clinical and Translational Science · doi:10.1111/cts.13187 · Cited by 8
Prediction and recommendation by machine learning through repetitive internal validation for hepatic veno-occlusive disease/sinusoidal obstruction syndrome and early death after allogeneic hematopoietic cell transplantation
Lee, S., Lee, E., Park, S., Park, M. S., Jung, J., Min, G. J., Park, S., Lee, S., Cho, B., Eom, K., et al. (2022) · Bone Marrow Transplantation · doi:10.1038/s41409-022-01583-z · Cited by 8
Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study
Clift, A. K., Hippisley‐Cox, J., Dodwell, D. J., Lord, S., Brady, M., Petrou, S., & Collins, P. G. S. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-050828 · Cited by 8
An Integrated Multilayered Framework for IoT Security Intrusion Decisions
Sallay, H. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.030791 · Cited by 8
Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning
Nakamura, K., Zhou, X. M., Sahara, N., Toyoda, Y., Enomoto, Y., Hara, H., Noro, M., Sugi, K., Huang, M. S., Moroi, M., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12122947 · Cited by 8
Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke
Lin, C.-H., Kuo, Y., Huang, Y.-C., Lee, M., Huang, Y.-W., Kuo, C., & Lee, J.-D. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043043 · Cited by 8
Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
Li, B., Yang, L., Jiang, C., Yao, Y., Li, H., Cheng, S., Zou, B., Fan, B., & Wang, L. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1052147 · Cited by 8
Deep-learning-based survival prediction of patients with cutaneous malignant melanoma
Yu, H., Yang, W., Wu, S., Xi, S., Xia, X., Zhao, Q., Ming, W., Wu, L., Hu, Y., Deng, L., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1165865 · Cited by 8
Deep Clustering Survival Machines with Interpretable Expert Distributions
Hou, B., Li, H., Jiao, Z., Zhou, Z. X., Zheng, H. R., & Yong Fan, A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230844 · Cited by 8
A self-supervised method for treatment recommendation in sepsis
Zhu, S., & Pu, J. (2021) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2000127 · Cited by 8
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesPreprint
Chen, G. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05285 · Cited by 7
A General Framework for Survival Analysis and Multi-State ModellingPreprint
Groha, S., Schmon, S. M., & Gusev, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04893 · Cited by 8
SurvBoard: Standardised Benchmarking for Multi-omics Cancer Survival ModelsPreprint
Wissel, D., Janakarajan, N., Grover, A., Toniato, E., Martínez, M. R., & Boeva, V. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.18.517043 · Cited by 8
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-TransformersPreprint
Arroyo, Á., Cartea, Á., Moreno-Pino, F., & Zohren, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4432087 · Cited by 11

Authors: Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y.
Affiliations: Yale University (US)
Yale New Haven Hospital (US)
Yale Cancer Center (US)
Corresponding author: Kluger, Yuval
ORCID: Katzman, Jared L.
Shaham, Uri
Cloninger, Alexander
Jiang, Tingting
Kluger, Yuval
Venue: BMC Medical Research Methodology
Publisher: Springer Science and Business Media LLC
Published: 2018-02-26
Volume: 18
Issue: 1
Pages: 24-24
ISSN: 1471-2288 (ISSN-L)
DOI: 10.1186/s12874-018-0482-1
PMC: PMC5828433
OpenAlex: W2753919178
Cited by: 1,995
References: 49
Citations by year: 2026: 376 · 2025: 386 · 2024: 321 · 2023: 301 · 2022: 238 · 2021: 206 · 2020: 94 · 2019: 54 · 2018: 18
FWCI: 52.41
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: BMC Medical Research Methodology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University)
Funders: National Human Genome Research Institute (1R01HG008383-01A1)
Directorate for Mathematical and Physical Sciences (DMS-1402254)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords: DeepSurv, Cox proportional hazards model, deep neural network, survival analysis, personalized treatment recommendations, treatment effectiveness, clinical trials
MeSH terms: Algorithms, Humans, Proportional Hazards Models, Neural Networks, Computer, Outcome Assessment, Health Care, Kaplan-Meier Estimate, Precision Medicine

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DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

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