Remaining useful life (RUL) prediction has been a topic of practical interest in many fields involving preventive intervention, including manufacturing, medicine and healthcare. While most of the conventional approaches suffer from censored failures arising and statistically circumscribed assumptions, few attempts have been made to predict RUL by developing a survival learning machine that explores the underlying relationship between time-varying prognostic variables and failure-free survival probability. This requires a purely data-driven prediction approach, devoid of any a survival model and all statistical assumptions. To this end, we propose a time-dependent survival neural network that additively estimates a latent failure risk and performs multiple binary classifications to generate prognostics of RUL-specific probability. We train the neural network by a new survival learning criterion that minimizes the censoring Kullback-Leibler divergence and guarantees monotonicity of the resulting probability. Experiments on four datasets demonstrate the great promise of our approach in real applications.

Time-Dependent Survival Neural Network for Remaining Useful Life Prediction

Feature-weighted survival learning machine for COPD failure prediction.

Feature prediction is necessary in different areas like Death Rate, Thunder Basin Antelope Study, Systolic Blood Pressure Data, General Psychology, child health, examination result, etc For that, we can collect previous information and study the relationship between the different facts Using the neurons concept, we can train our system to predict what must happen according to different situations Multiple regression models can be used to predict the death rate The key information of the article is the mathematical formulation for the forecast linear equation that estimates the multiple regression model calculation The value of dependent variable forecast under the influence of independent variables is explained

Prediction of Death Rate Using Regression Analysis

Survival prediction on time-to-event data associated with patients is crucial in clinical research Cox-type regression models are widely used for such prediction, but their performance for practical survival prediction suffers due to their use of a maximum partial likelihood estimator, which undermines the effectiveness and robustness of such models To address this problem, we propose to maximize a new full likelihood that fits the model to all of the data for both failed and censored patients We also represent time-to-event data by a new sequencing structure, which allows the proposed likelihood to be estimated by predicting event occurrence across the unit time intervals in practice Furthermore, the likelihood is regularized to prevent overfitting from arising in the model learning step We investigate the new approach via experimental studies on real-life clinical data and its superior performance compared to other popular state-of-the-art models reveals the great promise of our approach for clinical prediction

Sequential Representation of Clinical Data for Full-Fitting Survival Prediction

Survival classification on time-to-event clinical data is useful in clinical research. Survival analysis techniques are widely used to predict the survival probability over time that a particular event occurs. It is crucial to identify the risk of experiencing the event for patients, which can be achieved by classifying risk groups of patients. However, the existing classification methods, such as logistic regression, support vector machines and artificial neural network, cannot be applied to clinical data directly, due to the context of censoring. To address this problem, we propose a new two-tied labeling approach for reformulating the clinical data and make classifiers available to process such data. We perform such labeling on real-life clinical data and the good performances achieved by the classification methods reveal the great promise of our approach for risk group classification.

Survival Classification with Two-Tied Labeling of Censored Data

Chronic obstructive pulmonary disease (COPD) accounts for the highest rate of hospital readmissions and is the third leading cause of death in Canada, the United States and worldwide. Predicting COPD failure provides a prognostic warning of death or readmission, and is crucial to early intervention and decision-making. The aim of this study is to perform COPD failure prediction on longitudinal data. To address the inappropriate estimation of Cox hazard in current approaches, we propose a new representation of hazard to capture the relationship between survival probability and time-varying risk factors in a concise but effective way. To optimize model parameters, we design and maximize a new joint likelihood that comprises two components used to estimate survival status separately for failure and censored patients. A regularized optimization is performed on the joint likelihood to prevent overfitting arising from model learning. Our approach is applied to a real-life COPD data set and outperforms the current state-of-the-art prediction models in terms of the survival AUC, concordance index and Birer score metrics, this reveals that the great promise of our approach for clinical prediction.

Predicting COPD Failure by Modeling Hazard in Longitudinal Clinical Data

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Predicting COPD Failure by Modeling Hazard in Longitudinal Clinical Data

Sequential Representation of Clinical Data for Full-Fitting Survival Prediction