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

The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.

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

Open access

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

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1186/s12874-018-0482-1

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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.”
MethodInfluential

Show all 49 references

Sorted by citations

Items 101–200 of 1,000

Synthetic data for privacy-preserving clinical risk prediction
Qian, Z., Callender, T., Cebere, B., Janes, S. M., Navani, N., & van der Schaar, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-72894-y · Cited by 75
CapSurv: Capsule Network for Survival Analysis With Whole Slide Pathological Images
Tang, B., Li, A., Li, B., & Wang, M. (2019) · IEEE Access · doi:10.1109/access.2019.2901049 · Cited by 66
PathologyGAN: Learning Deep Representations of Cancer Tissue
Quiros, A. C., Murray‐Smith, R., & Yuan, K. (2020) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · Cited by 62
Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence
Westerlund, A. M., Hawe, J. S., Heinig, M., & Schunkert, H. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910291 · Cited by 63
Prediction and interpretation of cancer survival using graph convolution neural networks
Ramirez, R., Chiu, Y., Zhang, S., Ramirez, J., Chen, Y., Huang, Y., & Jin, Y.-F. (2021) · Methods · doi:10.1016/j.ymeth.2021.01.004 · Cited by 61
A Deep Active Survival Analysis approach for precision treatment recommendations: Application of prostate cancer
Nezhad, M. Z., Sadati, N., Yang, K., & Zhu, D. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.070 · Cited by 65
Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation
Qu, W., Tian, M., Lü, H., Zhou, Y., Liu, W., Tang, Z., Yao, Z., Huang, R., Zhu, G., Jiang, X., et al. (2023) · Hepatology International · doi:10.1007/s12072-023-10511-2 · Cited by 60
Machine learning for radiomics-based multimodality and multiparametric modeling
Wei, L., Osman, S. O. S., Hatt, M., & Naqa, I. E. (2019) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.19.03213-8 · Cited by 64
Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm
Kong, S. H., Lee, J.-W., Bae, B., Sung, J. K., Jung, K. H., Kim, J. H., & Shin, C. S. (2022) · Endocrinology and Metabolism · doi:10.3803/enm.2022.1461 · Cited by 59
Machine learning–based personalized prediction of gastric cancer incidence using the endoscopic and histologic findings at the initial endoscopy
Arai, J., Aoki, T., Sato, M., Niikura, R., Suzuki, N., Ishibashi, R., Tsuji, Y., Yamada, A., Hirata, Y., Ushiku, T., et al. (2022) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2021.12.033 · Cited by 59
A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0
Zhong, L., Fang, X., Dong, D., Peng, H., Fang, M., Huang, C., He, B., Lin, L. C., Ma, J., Tang, L., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.06.050 · Cited by 56
Failure modeling of water distribution pipelines using meta-learning algorithms
Almheiri, Z., Meguid, M. A., & Zayed, T. M. (2021) · Water Research · doi:10.1016/j.watres.2021.117680 · Cited by 57
Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records
Nigo, M., Rasmy, L., Mao, B., Kannadath, B. S., Xie, Z., & Zhi, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46211-0 · Cited by 59
2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma
Starke, S., Leger, S., Zwanenburg, A., Leger, K., Lohaus, F., Linge, A., Schreiber, A., Kalinauskaitė, G., Tinhofer, I., Guberina, N., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-70542-9 · Cited by 57
Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study
Yang, X., Qiu, H., Wang, L., & Wang, X. (2023) · Journal of Medical Internet Research · doi:10.2196/44417 · Cited by 65
Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer
Li, T., Wu, H., & Wang, M. D. (2020) · Methods · doi:10.1016/j.ymeth.2020.07.008 · Cited by 55
A deep survival interpretable radiomics model of hepatocellular carcinoma patients
Wei, L., Owen, D., Rosen, B., Guo, X., Cuneo, K. C., Lawrence, T. S., Haken, R. K. T., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.013 · Cited by 55
High-Dimensional Survival Analysis: Methods and Applications
Salerno, S. M., & Li, Y. (2022) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-032921-022127 · Cited by 66
Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations
Janßen, A., Bennis, F. C., & Mathôt, R. A. A. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14091814 · Cited by 57
TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Wang, F.-A., Zhuang, Z., Gao, F., He, R., Zhang, S., Wang, L., Liu, J., & Li, Y. (2024) · Genome Biology · doi:10.1186/s13059-024-03293-9 · Cited by 51
“The prognosis prediction model was established with the Cox survival regression network [46] to predict the risk score of individual patients, and the model was trained with weak supervision method.”
Method
A machine learning approach to leveraging electronic health records for enhanced omics analysis
Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S., Berson, E., Reiss, J., Kim, Y., Ghanem, M., Shu, C.-H., James, T., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00974-9 · Cited by 53
“…and the superior ability of COMET to predict days to the onset of labour. approaches have also been applied to traditional statistical frameworks, like the Cox proportional hazards model, which can be used to analyse time-to-event data, especially in datasets with censored patients 10 .”
A Fracture Risk Assessment Tool for High Resolution Peripheral Quantitative Computed Tomography
Whittier, D. E., Samelson, E. J., Hannan, M. T., Burt, L. A., Hanley, D. A., Biver, E., Szulc, P., Sornay‐Rendu, E., Merle, B., Chapurlat, R. D., et al. (2023) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4808 · Cited by 53
Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma
Ning, Z., Pan, W., Chen, Y., Xiao, Q., Zhang, X., Luo, J., Wang, J., & Zhang, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa056 · Cited by 53
Deep Learning-Based Recurrence Prediction of Atrial Fibrillation After Catheter Ablation.
Zhou, X., Nakamura, K., Sahara, N., Takagi, T., Toyoda, Y., Enomoto, Y., Hara, H., Noro, M., Sugi, K., Moroi, M., et al. (2021) · Circulation Journal · doi:10.1253/circj.cj-21-0622 · Cited by 40
Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study
Lee, K.-S., Jang, J.-Y., Yu, Y.-D., Heo, J. S., Han, H.-S., Yoon, Y.-S., Kang, C. M., Hwang, H. K., & Kang, S. (2021) · International Journal of Surgery · doi:10.1016/j.ijsu.2021.106050 · Cited by 55
SurvTRACE
Wang, Z., & Sun, J. (2022) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3535508.3545521 · Cited by 74
Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework
Azzi, S., Gagnon, S., Ramirez, A., & Richards, G. (2020) · Applied Sciences · doi:10.3390/app10186553 · Cited by 59
Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model
Deng, Y., Bucchianico, A. D., & Pechenizkiy, M. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.106727 · Cited by 53
Assessment of deep learning and transfer learning for cancer prediction based on gene expression data
Hanczar, B., Bourgeais, V., & Zehraoui, F. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04807-7 · Cited by 54
A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection
Yin, Q., Chen, W., Zhang, C., & Wei, Z. (2022) · Laboratory Investigation · doi:10.1038/s41374-022-00801-y · Cited by 51
Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis
Mirabnahrazam, G., Ma, D., Beaulac, C., Lee, S., Popuri, K., Lee, H., Cao, J., Galvin, J. E., Wang, L., & Beg, M. F. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.10.005 · Cited by 53
Prediction of conversion to Alzheimer’s disease using deep survival analysis of MRI images
Nakagawa, T., Ishida, M., Naito, J., Nagai, A., Yamaguchi, S., Onoda, K., & Initiative, O. B. O. T. A. D. N. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa057 · Cited by 53
AiIoMT: IoMT-Based System-Enabled Artificial Intelligence for Enhanced Smart Healthcare Systems
Awotunde, J. B., Folorunso, S. O., Ajagbe, S. A., Garg, J., & Ajamu, G. J. (2022) · Springer eBooks · doi:10.1007/978-3-030-80928-7_10 · Cited by 54
Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy
Trinh, L., Mingo, S., Vanstrum, E. B., Sanford, D. I., Aastha, A., Ma, R., Nguyen, J. H., Liu, Y., & Hung, A. J. (2021) · European Urology Focus · doi:10.1016/j.euf.2021.04.001 · Cited by 50
CAMR: cross-aligned multimodal representation learning for cancer survival prediction
Wu, X., Shi, Y., Wang, M., & Li, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad025 · Cited by 51
A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study
Zhang, F., Zhong, L., Zhao, X., Dong, D., Yao, J., Wang, S., Liu, Y., Zhu, D. L., Wang, Y., Wang, G., et al. (2020) · Therapeutic Advances in Medical Oncology · doi:10.1177/1758835920971416 · Cited by 49
Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography
Hou, K., Chen, J.-R., Wang, Y.-C., Chiu, M.-H., Lin, S.-P., Mo, Y., Peng, S.-C., & Lu, C. (2022) · Cancers · doi:10.3390/cancers14153798 · Cited by 48
Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: A multimethod study
Darbandi, M. R., Darbandi, M., Darbandi, S., Bado, I., Hadizadeh, M. R., & Khorshid, H. R. K. (2024) · European Journal of Cancer · doi:10.1016/j.ejca.2024.114227 · Cited by 57
A scoping review of artificial intelligence applications in clinical trial risk assessment
Teodoro, D., Naderi, N., Yazdani, A., Zhang, B., & Bornet, A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01886-7 · Cited by 56
“Only one study considered combined safety and ef ﬁ cacy risks 47 .”
Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs
Jing, B., Deng, Y., Zhang, T., Hou, D., Li, B., Qiang, M., Liu, K., Ke, L., Li, T., Sun, Y., et al. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105684 · Cited by 46
Genome‐wide association study‐based deep learning for survival prediction
Sun, T., Wei, Y., Chen, W., & Ding, Y. (2020) · Statistics in Medicine · doi:10.1002/sim.8743 · Cited by 48
Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis
Adeoye, J., Hui, L., Koohi‐Moghadam, M., Tan, J., Choi, S. W., & Thomson, P. J. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104635 · Cited by 46
Conformalized survival analysis
Candès, E. J., Lei, L., & Ren, Z. (2023) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkac004 · Cited by 79
Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer
Jung, J., Crnovrsanin, N., Wirsik, N. M., Nienhüser, H., Peters, L., Popp, F., Schulze, A., Wagner, M., Müller-Stich, B. P., Büchler, M. W., et al. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04063-5 · Cited by 45
SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data
Zhang, Y., Wong, G., Mann, G. J., Müller, S., & Yang, J. Y. H. (2022) · GigaScience · doi:10.1093/gigascience/giac071 · Cited by 51
A weighted random survival forest
Utkin, L. V., Konstantinov, A. V., Chukanov, V. S., Kots, M. V., Ryabinin, M. A., & Мелдо, А. А. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.015 · Cited by 45
Extreme learning machine Cox model for high‐dimensional survival analysis
Wang, H., & Li, G. (2019) · Statistics in Medicine · doi:10.1002/sim.8090 · Cited by 43
Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma
Byun, S., Heo, T.-S., Choi, J.-M., Jeong, Y. S., Kim, Y. S., Lee, W. K., & Kim, C. (2021) · Scientific Reports · doi:10.1038/s41598-020-80262-9 · Cited by 44
DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network
Lee, B., Chun, S. H., Hong, J. H., Woo, I. S., Kim, S., Jeong, J. W., Kim, J. J., Lee, H. W., Na, S. J., Beck, K. S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58722-z · Cited by 43
Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis
Yu, H., Huang, T., Feng, B., & Lyu, J. (2022) · BMC Cancer · doi:10.1186/s12885-022-09217-9 · Cited by 43
Multi-task learning for concurrent survival prediction and semi-supervised segmentation of gliomas in brain MRI
Wu, W., Yan, J., Zhao, Y., Sun, Q., Zhang, H., Cheng, J., Liang, D., Chen, Y., Zhang, Z., & Li, Z. (2023) · Displays · doi:10.1016/j.displa.2023.102402 · Cited by 43
Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature
Lee, M. (2023) · Biology · doi:10.3390/biology12070893 · Cited by 48
Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients
Wu, X., Zhang, S., Zhang, Z., He, Z., Xu, Z., Wang, W., Jin, Z., You, J., Guo, Y., Zhang, L., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00670-2 · Cited by 43
“Similar to the DeepSurv network 49 , the loss function was set to be the negative log partial likelihood: where N E =1 isthenumberofpatientswithanobservableeventand λ isthe ℓ 2 regularization parameter.”
Result
Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis
Lopatina, A., Ropele, S., Sibgatulin, R., Reichenbach, J. R., & Güllmar, D. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.609468 · Cited by 49
Role of Artificial Intelligence in Theranostics
Brosch-Lenz, J., Yousefirizi, F., Zukotynski, K., Beauregard, J.-M., Gaudet, V., Saboury, B., Rahmim, A., & Uribe, C. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.002 · Cited by 42
Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival
Pai, R. K., Banerjee, I., Shivji, S., Jain, S., Hartman, D., Buchanan, D. D., Jenkins, M. A., Schaeffer, D. F., Rosty, C., Como, J., et al. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.08.025 · Cited by 44
Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study
Yu, Y., Lee, K.-S., Kim, J. M., Ryu, J. H., Lee, J. G., Lee, K., Kim, B., & Kim, D. (2022) · International Journal of Surgery · doi:10.1016/j.ijsu.2022.106838 · Cited by 42
Survival cluster analysis
Chapfuwa, P., Li, C., Mehta, N., Carin, L., & Henao, R. (2020) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3368555.3384465 · Cited by 42
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds
Kovalev, M. S., & Utkin, L. V. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.007 · Cited by 43
Deep Learning-Based Survival Analysis for High-Dimensional Survival Data
Ha, I. D., Hao, L., Kim, J., & 권숙희 (2021) · Mathematics · doi:10.3390/math9111244 · Cited by 46
CT Radiomics–Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma
Park, S., Sham, J. G., Kawamoto, S., Blair, A. B., Rozich, N., Fouladi, D. F., Shayesteh, S., Hruban, R. H., He, J., Wolfgang, C. L., et al. (2021) · American Journal of Roentgenology · doi:10.2214/ajr.20.23490 · Cited by 42
Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma
Zhao, X., Liang, Y.-J., Zhang, X., Wen, D., Fan, W., Tang, L., Dong, D., Tian, J., & Mai, H. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05793-x · Cited by 42
Deep learning for the dynamic prediction of multivariate longitudinal and survival data
Lin, J. J., & Luo, S. (2022) · Statistics in Medicine · doi:10.1002/sim.9392 · Cited by 42
AI-driven precision diagnosis and treatment in Parkinson’s disease: a comprehensive review and experimental analysis
Twala, B. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1638340 · Cited by 49
“By simulating various scenarios and learning from patient feedback data, reinforcement learning algorithms can identify stimulation settings that maximize therapeutic benefits while minimizing side effects such as speech difficulties or mood disturbances (Katzman, 2018; Rosa et al., 2015).”
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Zheng, P., Yuan, S., & Wu, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011278 · Cited by 41
Machine Learning–Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative
Jamshidi, A., Pelletier, J., Labbe, A., Abram, F., Martel‐Pelletier, J., & Droit, A. (2021) · Arthritis Care & Research · doi:10.1002/acr.24601 · Cited by 41
Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models
Wang, M., Greenberg, M., Forkert, N. D., Chekouo, T., Afriyie, G., Ismail, Z., Smith, E. E., & Sajobi, T. T. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01754-y · Cited by 41
Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer’s disease progression
Lin, J. J., Li, K., & Luo, S. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220941532 · Cited by 40
Integrative survival analysis of breast cancer with gene expression and DNA methylation data
Bichindaritz, I., Liu, G., & Bartlett, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab140 · Cited by 41
Artificial intelligence for renal cancer: From imaging to histology and beyond
Kowalewski, K., Egen, L., Fischetti, C. E., Puliatti, S., Rivas, J. G., Taratkin, M. S., BELENCHON, I. R., Abate, M. A. S., Mühlbauer, J., Wessels, F., et al. (2022) · Asian journal of urology · doi:10.1016/j.ajur.2022.05.003 · Cited by 40
Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer
Yao, J., Cao, K., Hou, Y., Zhou, J. P., Xia, Y., Nogues, I., Song, Q., Jiang, H., Ye, X., Lu, J., et al. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005465 · Cited by 40
Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system
Zhang, Z., Huang, L., Li, J., & Wang, P. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04657-3 · Cited by 39
Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals
Minami, T., Sato, M., Toyoda, H., Yasuda, S., Yamada, T., Nakatsuka, T., Enooku, K., Nakagawa, H., Fujinaga, H., Izumiya, M., et al. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.05.042 · Cited by 43
MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features
Liu, M., Liu, Y., Cui, H., Li, C., & Ma, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385897 · Cited by 42
Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma
Vollmer, A., Hartmann, S., Vollmer, M., Shavlokhova, V., Brands, R. C., Kübler, A. C., Wollborn, J., Hassel, F., Couillard‐Després, S., Lang, G. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-56172-5 · Cited by 39
A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction
Teshale, A. B., Htun, H. L., Vered, M., Owen, A. J., & Freak‐Poli, R. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02087-7 · Cited by 42
“DeepSurv computes complex and non-linear features without a priori selection or domain expertise and is helpful for personalised risk prediction, even better than other linear and non-linear survival methods [78].”
Background
Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis
Hao, J., Kim, Y. S., Mallavarapu, T., Oh, J. H., & Kang, M. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621345 · Cited by 39
Deep learning‐based survival analysis for brain metastasis patients with the national cancer database
Bice, N., Kirby, N., Bahr, T. A., Rasmussen, K., Saenz, D. B., Wagner, T. D., Papanikolaou, N., & Fakhreddine, M. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12995 · Cited by 41
A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data
Wang, S., Zhang, H., Liu, Z., & Liu, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.800853 · Cited by 39
Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)
Hajjo, R., Sabbah, D. A., Bardaweel, S. K., & Tropsha, A. (2021) · Diagnostics · doi:10.3390/diagnostics11050742 · Cited by 38
Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis
Yan, L., Gao, N., Ai, F., Zhao, Y., Kang, Y., Chen, J., & Weng, Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.967758 · Cited by 38
Leveraging physiology and artificial intelligence to deliver advancements in health care
Zhang, A., Wu, Z., Wu, E., Wu, M. A., Snyder, M. P., Zou, J., & Wu, J. C. (2023) · Physiological Reviews · doi:10.1152/physrev.00033.2022 · Cited by 44
Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data
Mahootiha, M., Qadir, H. A., Bergsland, J., & Balasingham, I. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107978 · Cited by 38
Dynamic Survival Prediction Combining Landmarking with a Machine Learning Ensemble: Methodology and Empirical Comparison
Tanner, K., Sharples, L. D., Daniel, R., & Keogh, R. H. (2020) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12611 · Cited by 38
Deep Learning with Quantitative Features of Magnetic Resonance Images to Predict Biochemical Recurrence of Radical Prostatectomy: A Multi-Center Study
Yan, Y., Shao, L., Liu, Z., He, W., Yang, G., Liu, J., Xia, H., Zhang, Y., Chen, H., Liu, C., et al. (2021) · Cancers · doi:10.3390/cancers13123098 · Cited by 38
SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables
Hao, D., Li, Q., Feng, Q.-X., Qi, L., Liu, X.-S., Arefan, D., Zhang, Y., & Wu, S. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102424 · Cited by 37
GAN-based data augmentation for transcriptomics: survey and comparative assessment
Lacan, A., Sébag, M., & Hanczar, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad239 · Cited by 42
Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials
Wakkerman, F. C., Wu, J., Putter, H., Jurgenliemk-Schulz, I. M., Jobsen, J. J., Lutgens, L. C. H. W., Haverkort, M. A. D., de Jong, M., Mens, J. W., Wortman, B. G., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00142-6 · Cited by 37
Early stage NSCLS patients’ prognostic prediction with multi-information using transformer and graph neural network model
Lian, J., Deng, J., Hui, E. S. K., Koohi‐Moghadam, M., She, Y., Chen, C., & Vardhanabhuti, V. (2022) · eLife · doi:10.7554/elife.80547 · Cited by 37
Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha
Sahu, A. D., Wang, X., Munson, P. B., Klomp, J. P. G., Wang, X., Gu, S. S., Qian, G., Nicol, P., Zeng, Z., Wang, C., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0244 · Cited by 35
A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study
Wei, Z., Xv, Y., Liu, H., Li, Y., Yin, S., Xie, Y., Chen, Y., Lv, F., Jiang, Q., Li, F., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001194 · Cited by 36
PathologyGAN: Learning deep representations of cancer tissue
Quiros, A. C., Murray‐Smith, R., & Yuan, K. (2021) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2021-gfgg · Cited by 64
Artificial intelligence for the management of pancreatic diseases
Gorris, M., Hoogenboom, S. A. M., Wallace, M. B., & van Hooft, J. E. (2020) · Digestive Endoscopy · doi:10.1111/den.13875 · Cited by 36
Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology
Giudice, M. D., Peirone, S., Perrone, S., Priante, F., Varese, F., Tirtei, E., Fagioli, F., & Cereda, M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094563 · Cited by 36
Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong
Lee, S., Zhou, J., Leung, K. S. K., Wu, W. K. K., Wong, W. T., Liu, T., Wong, I. C. K., Jeevaratnam, K., Zhang, Q., & Tse, G. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001950 · Cited by 36
Strategies to Prevent Acute Kidney Injury after Pediatric Cardiac Surgery
den Eynde, J. V., Cloet, N., Lerberghe, R. V., Sá, M. P., Vlasselaers, D., Toelen, J., Verbakel, J. Y. J., Budts, W., Gewillig, M. H. M. L., Kutty, S., et al. (2021) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.05800421 · Cited by 36
Integrative analysis of clinical and epigenetic biomarkers of mortality
Huan, T., Nguyen, S., Colicino, E., Ochoa‐Rosales, C., Hill, W. D., Brody, J. A., Soerensen, M., Zhang, Y., Baldassari, A., Elhadad, M. A., et al. (2022) · Aging Cell · doi:10.1111/acel.13608 · Cited by 25
Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time
Cygu, S. B., Seow, H. Y., Dushoff, J. G., & Bolker, B. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-28393-7 · Cited by 35
Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction
Wang, Z., Lin, R., Li, Y., Zeng, J., Chen, Y., Ouyang, W., Li, H., Jia, X., Lai, Z., Yu, Y., et al. (2024) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbae012 · Cited by 44
Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma
Gomaa, A., Huang, Y., Hagag, A., Schmitter, C., Höfler, D., Weißmann, T., Breininger, K., Schmidt, M. A., Stritzelberger, J., Delev, D., et al. (2024) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdae122 · Cited by 35
“47,49 In addition to the imaging data, these datasets encompass clinical and molecular-pathologic information, such as gender, age, resection status, Isocitrate dehydrogenase (IDH) mutation status, and O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status.”
Background

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks