Relevant bibliographies by topics / Prediction of survival

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Prediction of survival'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Prediction of survival"

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Rau, Cheng-Shyuan, Shao-Chun Wu, Jung-Fang Chuang, Chun-Ying Huang, Hang-Tsung Liu, Peng-Chen Chien, and Ching-Hua Hsieh. "Machine Learning Models of Survival Prediction in Trauma Patients." Journal of Clinical Medicine 8, no. 6 (June 5, 2019): 799. http://dx.doi.org/10.3390/jcm8060799.

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Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.
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Riviere, Paul, Christopher Tokeshi, Jiayi Hou, Vinit Nalawade, Reith Sarkar, Anthony J. Paravati, Melody Schiaffino, Brent Rose, Ronghui Xu, and James D. Murphy. "Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–7. http://dx.doi.org/10.1200/cci.18.00111.

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PURPOSE Treatment decisions about localized prostate cancer depend on accurate estimation of the patient’s life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient’s health. METHODS We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model’s predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.
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Copeland-Fields, L., T. Griffin, T. Jenkins, M. Buckley, and LC Wise. "Comparison of outcome predictions made by physicians, by nurses, and by using the Mortality Prediction Model." American Journal of Critical Care 10, no. 5 (September 1, 2001): 313–19. http://dx.doi.org/10.4037/ajcc2001.10.5.313.

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BACKGROUND: Critical care nurses must collaborate with physicians, patients, and patients' families when making decisions about aggressiveness of care. However, few studies address nurses' ability to predict outcomes. OBJECTIVES: To compare predictions of survival outcomes made by nurses, by physicians, and by using the Mortality Prediction Model. METHODS: Predictions of survival and function and attitudes toward aggressiveness of care based on the predictions were recorded on questionnaires in the emergency department by emergency and intensive care unit physicians and by intensive care unit nurses at the time of admission to the unit between February and September 1995 for 235 consecutive adult nontrauma patients. Scores on the Mortality Prediction Model were calculated on admission. Data on 85 of the 235 patients were analyzed by using descriptive, chi 2, and correlational statistics. Nurses' predictions of function were compared with patients' actual outcomes 6 months after admission. RESULTS: Nurses' predictions of survival were comparable to those of emergency physicians and superior to those obtained by using the objective tool. Years of nursing experience had no relationship to attitudes toward aggressiveness of care. Nurses accurately predicted functional outcomes in 52% of the followed-up cases. Intensive care physicians were more accurate than nurses and emergency physicians in predicting survival. All predictions made by clinicians were superior to those obtained by using the model. CONCLUSIONS: Nurses can predict survival outcomes as accurately as physicians do. Greater sensitivity and specificity are necessary before clinical judgment or predictive tools can be considered as screens for determining aggressiveness of care.
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Karakiewicz, Pierre I., Alberto Briganti, Felix K. H. Chun, Quoc-Dien Trinh, Paul Perrotte, Vincenzo Ficarra, Luca Cindolo, et al. "Multi-Institutional Validation of a New Renal Cancer–Specific Survival Nomogram." Journal of Clinical Oncology 25, no. 11 (April 10, 2007): 1316–22. http://dx.doi.org/10.1200/jco.2006.06.1218.

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Purpose We tested the hypothesis that the prediction of renal cancer–specific survival can be improved if traditional predictor variables are used within a prognostic nomogram. Patients and Methods Two cohorts of patients treated with either radical or partial nephrectomy for renal cortical tumors were used: one (n = 2,530) for nomogram development and for internal validation (200 bootstrap resamples), and a second (n = 1,422) for external validation. Cox proportional hazards regression analyses modeled the 2002 TNM stages, tumor size, Fuhrman grade, histologic subtype, local symptoms, age, and sex. The accuracy of the nomogram was compared with an established staging scheme. Results Cancer-specific mortality was observed in 598 (23.6%) patients, whereas 200 (7.9%) died as a result of other causes. Follow-up ranged from 0.1 to 286 months (median, 38.8 months). External validation of the nomogram at 1, 2, 5, and 10 years after nephrectomy revealed predictive accuracy of 87.8%, 89.2%, 86.7%, and 88.8%, respectively. Conversely, the alternative staging scheme predicting at 2 and 5 years was less accurate, as evidenced by 86.1% (P = .006) and 83.9% (P = .02) estimates. Conclusion The new nomogram is more contemporary, provides predictions that reach further in time and, compared with its alternative, which predicts at 2 and 5 years, generates 3.1% and 2.8% more accurate predictions, respectively.
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Schumacher, M., E. Graf, and T. Gerds. "How to Assess Prognostic Models for Survival Data: A Case Study in Oncology." Methods of Information in Medicine 42, no. 05 (2003): 564–71. http://dx.doi.org/10.1055/s-0038-1634384.

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Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.
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Zeng, Yuan, Wenhua Liang, and Jian He. "Association of postoperative radiotherapy with survival in patients with completely resected pathologic N2 non-small cell lung cancer treated with chemotherapy." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e21008-e21008. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21008.

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e21008 Background: The current staging system for completely resected pathological N2 non-small-cell lung cancer (NSCLC) treated with chemotherapy is not suitable for predicting those patients most likely to benefit or not from post-operative radiotherapy (PORT). This study aimed to construct a survival prediction model that will enable individualized predictions of the net survival of PORT. Methods: A total of 3094 cases between 2002 and 2014 were extracted from the Surveillance, Epidemiology, and End Results databases. Patient characteristics were included as covariates, and their association for overall survival (OS) with and witout PORT was assessed. Externally validate data of 602 patients were included from China. Results: A total of 3696 patients were included for analysis. Age, gender, examined lymph node, positive lymph node, tumor size, extent of surgery, and visceral pleural invasion were significantly associated with OS, with P < .05. The calibration curve for OS showed great agreement between prediction by survival prediction model and actual observation.In the training cohort, the C-index for OS was 0.619 (95% CI, 0.598-0.641) in the PORT group and 0.627 (95% CI, 0.605-0.648) in the non-PORT group. In the externally validation cohort, the C-index for OS was 0.599 (95% CI, 0.485- 0.713) in the PORT group and 0.595 (95% CI, 0.544-0.646) in non-PORT group. The two survival prediction models were developed based on clinical variables to estimate an individual's net survival gain or not attributable to PORT. We found that PORT could improve OS (HR, 0.861; P = 0.044) for patients with a positive PORT net survival benefit. Conclusions: We established a practical survival prediction model that can be used to make individualized predictions about the expected survival of PORT or without PORT in patients with completely resected pathological N2 NSCLC, treated with chemotherapy.
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Hansen, Maiken B., Lone Ross Nylandsted, Morten A. Petersen, Mathilde Adsersen, Leslye Rojas-Concha, and Mogens Groenvold. "Patient-reported symptoms and problems at admission to specialized palliative care improved survival prediction in 30,969 cancer patients: A nationwide register-based study." Palliative Medicine 34, no. 6 (March 18, 2020): 795–805. http://dx.doi.org/10.1177/0269216320908488.

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Background: Large, nationally representative studies of the association between quality of life and survival time in cancer patients in specialized palliative care are missing. Aim: The aim of this study was to investigate whether symptoms/problems at admission to specialized palliative care were associated with survival and if the symptoms/problems may improve prediction of death within 1 week and 1 month, respectively. Setting/participants: All cancer patients who had filled in the EORTC QLQ-C15-PAL at admission to specialized palliative care in Denmark in 2010–2017 were included through the Danish Palliative Care Database. Cox regression was used to identify clinical variables (gender, age, type of contact (inpatient vs outpatient), and cancer site) and symptoms/problems significantly associated with survival. To test whether symptoms/problems improved survival predictions, the overall accuracy (area under the receiver operating characteristic curve) for different prediction models was compared. The validity of the prediction models was tested with data on 5,508 patients admitted to palliative care in 2018. Results: The study included 30,969 patients with an average age of 68.9 years; 50% were women. Gender, age, type of contact, cancer site, and most symptoms/problems were significantly associated with survival time. The predictive value of symptoms/problems was trivial except for physical function, which clearly improved the overall accuracy for 1-week and 1-month predictions of death when added to models including only clinical variables. Conclusion: Most symptoms/problems were significantly associated with survival and mainly physical function improved predictions of death. Interestingly, the predictive value of physical function was the same as all clinical variables combined (in hospice) or even higher (in palliative care teams).
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Chowdary, Raj P., Sean P. Campbell, Michael Rosenberg, and Norman E. Hugo. "Dermofluorometric Prediction of Flap Survival." Annals of Plastic Surgery 19, no. 2 (August 1987): 154–57. http://dx.doi.org/10.1097/00000637-198708000-00008.

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Fricker, Janet. "Survival prediction with neuroendocrine differentiation?" Lancet Oncology 7, no. 11 (November 2006): 891. http://dx.doi.org/10.1016/s1470-2045(06)70921-4.

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Tuhrim, Stanley, James M. Dambrosia, Thomas R. Price, Jay P. Mohr, Philip A. Wolf, Albert Heyman, and Carlos S. Kase. "Prediction of intracerebral hemorrhage survival." Annals of Neurology 24, no. 2 (August 1988): 258–63. http://dx.doi.org/10.1002/ana.410240213.

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Dissertations / Theses on the topic "Prediction of survival"

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Parast, Layla. "Landmark Prediction of Survival." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10085.

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The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings the onset time of an observable intermediate event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long term survival. Throughout this dissertation, we examine an approach to incorporate intermediate event information for the prediction of long term survival: the landmark model. In Chapter 1, we use the landmark modeling framework to develop procedures to assess how a patient’s long term survival trajectory may change over time given good intermediate outcome indications along with prognosis based on baseline markers. We propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. We illustrate our proposed procedures using a breast cancer dataset. In Chapter 2, we aim to incorporate intermediate event time information for the prediction of survival. We propose a fully non-parametric procedure to incorporate intermediate event information when only a single baseline discrete covariate is available for prediction. When a continuous covariate or multiple covariates are available, we propose to incorporate intermediate event time information using a flexible varying coefficient model. To evaluate the performance of the resulting landmark prediction rule and quantify the information gained by using the intermediate event, we use robust non-parametric procedures. We illustrate these procedures using a dataset of post-dialysis patients with end-stage renal disease. In Chapter 3, we consider improving efficiency by incorporating intermediate event information in a randomized clinical trial setting. We propose a semi-nonparametric two-stage procedure to estimate survival by incorporating intermediate event information observed before the landmark time. In addition, we present a testing procedure using these resulting estimates to test for a difference in survival between two treatment groups. We illustrate these proposed procedures using an AIDS dataset.
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Jones, Margaret. "Point prediction in survival time models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340616.

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Aparicio, Vázquez Ignacio. "Venn Prediction for Survival Analysis : Experimenting with Survival Data and Venn Predictors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278823.

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The goal of this work is to expand the knowledge on the field of Venn Prediction employed with Survival Data. Standard Venn Predictors have been used with Random Forests and binary classification tasks. However, they have not been utilised to predict events with Survival Data nor in combination with Random Survival Forests. With the help of a Data Transformation, the survival task is transformed into several binary classification tasks. One key aspect of Venn Prediction are the categories. The standard number of categories is two, one for each class to predict. In this work, the usage of ten categories is explored and the performance differences between two and ten categories are investigated. Seven data sets are evaluated, and their results presented with two and ten categories. For the Brier Score and Reliability Score metrics, two categories offered the best results, while Quality performed better employing ten categories. Occasionally, the models are too optimistic. Venn Predictors rectify this performance and produce well-calibrated probabilities.
Målet med detta arbete är att utöka kunskapen om området för Venn Prediction som används med överlevnadsdata. Standard Venn Predictors har använts med slumpmässiga skogar och binära klassificeringsuppgifter. De har emellertid inte använts för att förutsäga händelser med överlevnadsdata eller i kombination med Random Survival Forests. Med hjälp av en datatransformation omvandlas överlevnadsprediktion till flera binära klassificeringsproblem. En viktig aspekt av Venn Prediction är kategorierna. Standardantalet kategorier är två, en för varje klass. I detta arbete undersöks användningen av tio kategorier och resultatskillnaderna mellan två och tio kategorier undersöks. Sju datamängder används i en utvärdering där resultaten presenteras för två och tio kategorier. För prestandamåtten Brier Score och Reliability Score gav två kategorier de bästa resultaten, medan för Quality presterade tio kategorier bättre. Ibland är modellerna för optimistiska. Venn Predictors korrigerar denna prestanda och producerar välkalibrerade sannolikheter.
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Negassa, Abdissa. "Validation of tree-structured prediction for censored survival data." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40407.

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Objectives. (i) to develop a computationally efficient algorithm of tree-growing for censored survival data, (ii) to assess the performance of two validation schemes, and (iii) to evaluate the performance of computationally inexpensive model selection criteria in relation to cross-validation.
Background. In the tree-growing literature, a number of computationally inexpensive model selection criteria were suggested; however, none of them were systematically investigated for their performance. RECursive Partition and AMalgamation (RECPAM) is one of the existing tree-growing algorithms that provide such built-in model selection criteria. Application of RECPAM's different model selection criteria leads to a wide range of models (40). Since RECPAM is an exploratory data analysis tool, it is desirable to reduce its computational cost and establish the general properties of its model selection criteria so that clear guidelines can be suggested.
Methods. A computationally efficient tree-growing algorithm for prognostic classification and subgroup analysis is developed by employing the Cox score statistic and the Mantel-Haenszel estimator of the relative hazard. Two validation schemes, restricting validation to pruning and parameter estimation and validating the whole process of tree growing, are implemented and evaluated in simulation. Three model selection criteria--the elbow approach, minimum Akaike Information Criterion (AIC), and the one standard error (ISE) rule--were compared to cross-validation under a broad range of scenarios using simulation. Examples of medical data analyses are presented.
Conclusions. A gain in computational efficiency is achieved while obtaining the same result as the original RECPAM approach. The restricted validation scheme is computationally less expensive, however, it is biased. In the case of subgroup analysis, to adjust properly for influential prognostic factors, we suggest constructing a prognostic classification on such factors and using the resulting classification as strata in conducting the subgroup analysis. None of the model selection criteria studied exhibit a consistently superior performance over the range of scenarios considered here. Therefore, we propose a two-stage model selection strategy in which cross-validation is employed at the first step, and if according to this step there is evidence of structure in the data set, then the elbow rule is recommended in the second step.
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Zhang, Haonan. "Machine Learning Approaches for Prediction of Kidney Transplant Survival." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1555953011881185.

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Spencer, David James. "Predicting early failure on probation using survival analysis and psychological predictor variables /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.

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Raoufi-Danner, Torrin. "Effects of Missing Values on Neural Network Survival Time Prediction." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150339.

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Data sets with missing values are a pervasive problem within medical research. Building lifetime prediction models based solely upon complete-case data can bias the results, so imputation is preferred over listwise deletion. In this thesis, artificial neural networks (ANNs) are used as a prediction model on simulated data with which to compare various imputation approaches. The construction and optimization of ANNs is discussed in detail, and some guidelines are presented for activation functions, number of hidden layers and other tunable parameters. For the simulated data, binary lifetime prediction at five years was examined. The ANNs here performed best with tanh activation, binary cross-entropy loss with softmax output and three hidden layers of between 15 and 25 nodes. The imputation methods examined are random, mean, missing forest, multivariate imputation by chained equations (MICE), pooled MICE with imputed target and pooled MICE with non-imputed target. Random and mean imputation performed poorly compared to the others and were used as a baseline comparison case. The other algorithms all performed well up to 50% missingness. There were no statistical differences between these methods below 30% missingness, however missing forest had the best performance above this amount. It is therefore the recommendation of this thesis that the missing forest algorithm is used to impute missing data when constructing ANNs to predict breast cancer patient survival at the five-year mark.
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Tian, Shaonan. "Essays on Corporate Default Prediction." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1352403546.

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Orth, Walter [Verfasser]. "Multi-Period Credit Default Prediction : A Survival Analysis Approach / Walter Orth." Aachen : Shaker, 2012. http://d-nb.info/1066196826/34.

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Kaponen, Martina. "Prediction of survival time of prostate cancer patients using Cox regression." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354482.

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Books on the topic "Prediction of survival"

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Hein, Putter, ed. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press, 2012.

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Science of survival: Prediction of human behavior. Los Angeles, Calif: Bridge Publications, 2001.

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Ron, Hubbard L. Science of survival: Prediction of human behavior. Copenhagen K, Denmark: New Era Publications International Aps, 1993.

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Peter, Schmidt. Predicting recidivism using survival models. New York: Springer-Verlag, 1988.

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Smith, Charles Hugh. Survival +: Structuring prosperity for yourself and the nation. Berkeley, Calif: Oftwominds, 2009.

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Cooper, Arnold C. A resource-based prediction of new venture survival and growth. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1991.

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Cherdanceva, Tat'yana, Vladimir Klimechev, and Igor' Bobrov. Pathological and molecular biological analysis of renal cell carcinoma. Diagnosis and prognosis. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1020785.

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The monograph is devoted to the study of pathomorphological and molecular-biological characteristics of renal cell carcinoma and peritumoral zone depending on the degree of malignancy, and determine prognostic significance of criteria for predicting the postoperative survival of patients. Of interest to urologists, oncologists, pathologists, researchers, graduate students, dealing with the diagnosis of renal cell carcinoma and subsequent prediction of postoperative survival of patients.
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Schmidt, Peter, and Ann Dryden Witte. Predicting Recidivism Using Survival Models. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3772-3.

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Hurd, Michael D. The predictive validity of subjective probabilities of survival. Cambridge, MA: National Bureau of Economic Research, 1997.

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Chow, Edward. A predictive model for survival in metastatic cancer patients attending an out-patient palliative radiotherapy clinic. Ottawa: National Library of Canada, 2001.

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Book chapters on the topic "Prediction of survival"

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Shboul, Zeina A., Mahbubul Alam, Lasitha Vidyaratne, Linmin Pei, and Khan M. Iftekharuddin. "Glioblastoma Survival Prediction." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 508–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11726-9_45.

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Johnson, Wesley O. "Survival Analysis for Interval Data." In Diagnosis and Prediction, 75–90. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1540-0_5.

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Shboul, Zeina A., Lasitha Vidyaratne, Mahbubul Alam, and Khan M. Iftekharuddin. "Glioblastoma and Survival Prediction." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 358–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75238-9_31.

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Emura, Takeshi, Shigeyuki Matsui, and Virginie Rondeau. "Personalized Dynamic Prediction of Survival." In Survival Analysis with Correlated Endpoints, 77–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3516-7_5.

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Rajput, Snehal, Rupal Agravat, Mohendra Roy, and Mehul S. Raval. "Glioblastoma Multiforme Patient Survival Prediction." In Lecture Notes in Electrical Engineering, 47–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3880-0_6.

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Kim, Sundong, Hwanjun Song, Sejin Kim, Beomyoung Kim, and Jae-Gil Lee. "Revisit Prediction by Deep Survival Analysis." In Advances in Knowledge Discovery and Data Mining, 514–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_39.

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Agravat, Rupal R., and Mehul S. Raval. "Brain Tumor Segmentation and Survival Prediction." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 338–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46640-4_32.

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Akbar, Agus Subhan, Chastine Fatichah, and Nanik Suciati. "Modified MobileNet for Patient Survival Prediction." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 374–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72087-2_33.

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Johnson, Wesley O. "Predictive Influence in the Log Normal Survival Model." In Modelling and Prediction Honoring Seymour Geisser, 104–21. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2414-3_6.

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Valentini, Vincenzo, Andrea Damiani, Andre Dekker, and Nicola Dinapoli. "Statistics of Survival Prediction and Nomogram Development." In Decision Tools for Radiation Oncology, 7–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/174_2013_854.

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Conference papers on the topic "Prediction of survival"

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Bostrom, Henrik, Lars Asker, Ram Gurung, Isak Karlsson, Tony Lindgren, and Panagiotis Papapetrou. "Conformal Prediction Using Random Survival Forests." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.00-57.

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Huang, Chenglong, Albert Zhang, and Guanghua Xiao. "Deep Integrative Analysis for Survival Prediction." In Pacific Symposium on Biocomputing 2018. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813235533_0032.

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Sokota, Samuel, Ryan D'Orazio, Khurram Javed, Humza Haider, and Russell Greiner. "Simultaneous Prediction Intervals for Patient-Specific Survival Curves." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/828.

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Abstract:
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models -- known as individual survival distributions (ISDs) -- produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results. Furthermore, we introduce both a modification to the existing method and a novel method for estimating simultaneous prediction intervals and show that they offer competitive performance. It is worth emphasizing that these methods are not limited to survival analysis and can be applied in any context in which sampling the distribution of interest is tractable. Code is available at https://github.com/ssokota/spie.
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Soria, E., J. D. Martin, J. Caravaca, A. J. Serrano, M. Martinez, R. Magdalena, J. Gomez, M. Heras, and G. Sanz. "Survival prediction in patients undergoing ischemic cardiopathy." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178839.

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Guangliang Gao, Zhan Bu, Lingbo Liu, Jie Cao, and Zhiang Wu. "A survival analysis method for stock market prediction." In 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC). IEEE, 2015. http://dx.doi.org/10.1109/besc.2015.7365968.

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Zhou, Mu, Lawrence O. Hall, and Dmitry B. Goldgof. "Exploring Brain Tumor Heterogeneity for Survival Time Prediction." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.110.

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Fulian, Yin, Jiang Yueqi, Su Pei, and Su Ge. "Research on Video Ratings Prediction and Survival Analysis." In 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE, 2017. http://dx.doi.org/10.1109/iccsec.2017.8446821.

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Zhu, Qianwen, Jiaxing Shang, Xinjun Cai, Linli Jiang, Feiyi Liu, and Baohua Qiang. "CoxRF: Employee Turnover Prediction Based on Survival Analysis." In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2019. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00212.

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Agravat, Rupal R., and Mehul S. Raval. "Prediction of Overall Survival of Brain Tumor Patients." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929497.

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Malik, Vidhi, Shayoni Dutta, Yogesh Kalakoti, and Durai Sundar. "Multi-omics Integration based Predictive Model for Survival Prediction of Lung Adenocarcinaoma." In 2019 Grace Hopper Celebration India (GHCI). IEEE, 2019. http://dx.doi.org/10.1109/ghci47972.2019.9071831.

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Reports on the topic "Prediction of survival"

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Lukaszek, W., W. Dixon, M. Vella, C. Messick, S. Reno, and J. Shideler. Characterization of wafer charging mechanisms and oxide survival prediction methodology. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10118587.

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Tsodikov, Alexander. Short- and Long-Term Effects in Prostate Cancer Survival: Analysis of Treatment Efficacy and Risk Prediction. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada437726.

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Tsodikov, Alexander D. Short- and Long-Term Effects in Prostate Cancer Survival: Analysis of Treatment Efficacy and Risk Prediction. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada455402.

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Tsodikov, Alexander. Short- and Long-Term Effects in Prostate Cancer Survival: Analysis of Treatment Efficacy and Risk Prediction. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada421695.

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Hurd, Michael, and Kathleen McGarry. The Predictive Validity of Subjective Probabilities of Survival. Cambridge, MA: National Bureau of Economic Research, September 1997. http://dx.doi.org/10.3386/w6193.

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Schmidt, Peter, and Ann Dryden Witte. Predicting Criminal Recidivism Using "Split Population" Survival Time Models. Cambridge, MA: National Bureau of Economic Research, November 1987. http://dx.doi.org/10.3386/w2445.

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Keyomarsi, Khandan. Cyclin E, a Powerful Predictor of Survival in Breast Cancer - A Prospective Study. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada419793.

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Su, Tsung-Chow, R. Q. Robe, and Duncan J. Finlayson. On Predicting the Leeway and Drift of A Survival Suit Clad Person In-Water. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada348357.

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Punglia, Rinaa, Natasha Stout, Angel Cronin, Hajime Uno, Elissa Ozanne, Michael Hassett, Elizabeth Frank, Deborah Schrag, Caprice Greenberg, and Djora Soeteman. Predicting the Impact of Treatment Options on Survival and Breast Conservation in Patients With Ductal Carcinoma In Situ(DCIS). Patient-Centered Outcomes Research Institute® (PCORI), January 2020. http://dx.doi.org/10.25302/1.2020.ce.12114173.

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Beer, W. Nicholas, Susannah Iltis, and James J. Anderson. Evaluation of the 2008 Predictions of Run-Timing and Survival of Wild Migrant Yearling Chinook and Steelhead on the Columbia and Snake Rivers. Office of Scientific and Technical Information (OSTI), January 2009. http://dx.doi.org/10.2172/947611.

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