Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Yang, Rui; Wang, Zichen; Huang, Wei; Feng, Aozi; Li, Li; Lyu, Jun

doi:10.1155/2021/5745304

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…CoxPH is currently the most frequently employed model, but its accuracy is restricted (34) due to the linearity of its factors. The DeepSurv method (38)(39)(40).…”

Section: Discussionmentioning

confidence: 99%

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Yang

et al. 2023

Front. Med.

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BackgroundThis study obtained data on patients with cutaneous malignant melanoma (CMM) from the Surveillance, Epidemiology, and End Results (SEER) database, and used a deep learning and neural network (DeepSurv) model to predict the survival rate of patients with CMM and evaluate its effectiveness.MethodsWe collected information on patients with CMM between 2004 and 2015 from the SEER database. We then randomly divided the patients into training and testing cohorts at a 7:3 ratio. The likelihood that patients with CMM will survive was forecasted using the DeepSurv model, and its results were compared with those of the Cox proportional-hazards (CoxPH) model. The calibration curves, time-dependent area under the receiver operating characteristic curve (AUC), and concordance index (C-index) were used to assess the prediction abilities of the model.ResultsThis study comprised 37,758 patients with CMM: 26,430 in the training cohort and 11,329 in the testing cohort. The CoxPH model demonstrated that the survival of patients with CMM was significantly influenced by age, sex, marital status, summary stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, tumor size, and tumor extension. The C-index of the CoxPH model was 0.875. We also constructed the DeepSurv model using the data from the training cohort, and its C-index was 0.910. We examined how well the aforementioned two models predicted outcomes. The 1-, 3-, and 5-year AUCs were 0.928, 0.837, and 0.855, respectively, for the CoxPH model, and 0.971, 0.947, and 0.942 for the DeepSurv model. The DeepSurv model presented a greater predictive effect on patients with CMM, and its reliability was better than that of the CoxPH model according to both the AUC value and the calibration curve.ConclusionThe DeepSurv model, which we developed based on the data of patients with CMM in the SEER database, was found to be more effective than the CoxPH model in predicting the survival time of patients with CMM.

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“…CoxPH is currently the most frequently employed model, but its accuracy is restricted (34) due to the linearity of its factors. The DeepSurv method (38)(39)(40).…”

Section: Discussionmentioning

confidence: 99%

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Yang

et al. 2023

Front. Med.

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“…Multiple investigations have demonstrated the superior performance of the DeepSurv model over conventional linear prediction models in prognosticating survival outcomes 17 . Notably, empirical evidence substantiates the heightened accuracy of the DeepSurv model compared to the CoxPH model in diverse malignancies, encompassing lung cancer, colon adenocarcinoma, and patients within Coronary Care Units [27][28][29][30][31] .…”

Section: Discussionmentioning

confidence: 98%

Deep-Learning-Based Survival Prediction of Patients with Thyroid Cancer

Zhang,

Li,

Zheng

et al. 2024

Preprint

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Background This study acquired data pertaining to individuals diagnosed with Thyroid Cancer (TC) from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, a deep learning and neural network model known as DeepSurv was employed to forecast the survival rate of TC patients and assess its efficacy. Methods Information on individuals diagnosed with TC from the years 2000 to 2019 was collected from the SEER database. The individuals in question were subsequently allocated into training and testing cohorts through a random selection process, maintaining a ratio of 7:3. The outcomes of the DeepSurv model were compared to those of the Cox proportional-hazards (CoxPH) model in order to estimate the chances of survival for TC patients. The accuracy of the model's predictions was evaluated through the examination of calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index). Results A total of 25,797 individuals diagnosed with TC were included in this study, with 18,057 comprising the training group and 7,740 forming the testing cohort. The CoxPH model exhibited robust correlations between age, gender, marital status, surgical intervention, radiation therapy, tumor extension, and the survival outcomes of TC patients. Notably, the C-index for the CoxPH model was 0.884, indicating a high level of predictive accuracy. Additionally, the training cohort data were used to create the DeepSurv model, which produced a higher C-index of 0.904. The predictive performance of both models was assessed, and the 3-, 5-, and 8-year AUC values were calculated. Regarding the CoxPH model, the corresponding area under the receiver operating characteristic curve (AUC) values were determined to be 0.835, 0.797, and 0.756, respectively. In comparison, the DeepSurv model achieved higher AUC values of 0.942, 0.918, and 0.906. The DeepSurv model demonstrated superior predictive ability for TC patients, as indicated by both the AUC values and the calibration curve, suggesting higher reliability compared to the CoxPH model. Conclusion Using TC patient data from the SEER database for research, we built the DeepSurv model, which performed better than the CoxPH model in estimating the survival time of TC patients.

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“…It also can leverage diverse data sources, including clinical records, histopathological data, and genetic pro les. Integrating this information can provide a more comprehensive picture of the patient's condition and potential risk factors 22,23 .…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning-Based Survival Prediction of Patients with lower limb melanoma

Zhang,

Yu,

Zheng

et al. 2023

Preprint

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Background For the purpose to examine lower limb melanoma (LLM) and its long-term survival rate, we used data from the Surveillance, Epidemiology and End Results (SEER) database. To estimate the prognosis of LLM patients and assess its efficacy, we used a powerful deep learning and neural network approach called DeepSurv. Methods: We gathered data on those who had an LLM diagnosis between 2000 and 2019 from the SEER database. We divided the people into training and testing cohorts at a 7:3 ratio using a random selection technique. To assess the likelihood that LLM patients would survive, we compared the results of the DeepSurv model with those of the Cox proportional-hazards (CoxPH) model. Calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index) were all used to assess how accurate the predictions were. Results: In this study, a total of 26,243 LLM patients were enrolled, with 7,873 serving as the testing cohort and 18,370 as the training cohort. Significant correlations with age, gender, AJCC stage, chemotherapy status, surgery status, regional lymph node removal and the survival outcomes of LLM patients were found by the CoxPH model. The CoxPH model's C-index was 0.766, which signifies a good degree of predicted accuracy. Additionally, we created the DeepSurv model using the training cohort data, which had a higher C-index of 0.852. In addition to calculating the 3-, 5-, and 8-year AUC values, the predictive performance of both models was evaluated. The equivalent AUC values for the CoxPH model were 0.795, 0.767, and 0.847, respectively. The DeepSurv model, in comparison, had better AUC values of 0.872, 0.858, and 0.847. In comparison to the CoxPH model, the DeepSurv model demonstrated greater prediction performance for LLM patients, as shown by the AUC values and the calibration curve. Conclusion: We created the DeepSurv model using LLM patient data from the SEER database, which performed better than the CoxPH model in predicting the survival time of LLM patients.

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Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

Cited by 8 publications

References 29 publications

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Deep-Learning-Based Survival Prediction of Patients with Thyroid Cancer

Deep-Learning-Based Survival Prediction of Patients with lower limb melanoma

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