Prognostics or early detection of incipient faults is an important industrial challenge for condition-based and preventive maintenance. Physics-based approaches to modeling fault progression are infeasible due to multiple interacting components, uncontrolled environmental factors and observability constraints. Moreover…
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48 results for “prognostic factors”
In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a…
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to be promoted from the level of mere associations to being able to answer causal questions. We present a scenario with real-world medical image…
DARTS optimizes covariate selection in trials with limited data.
problem Limited budget for high-dimensional pretreatment data.
method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).
result DARTS efficiently concentrates budget on informative features.
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
problem Weak predictive accuracy and limited interpretability of psychiatric questionnaires.
method Two-stage method: stable preprocessing followed by a linear mapping.
result REFINE outperforms other interpretable approaches in psychiatric and non-psychiatric prediction tasks.
Bayesian method improves clinical trial efficiency.
problem Increase treatment effect estimates in clinical trials.
method Combines prognostic covariate adjustment with a Bayesian framework.
result Substantial increase in statistical power with controlled type I error.
Enhances PHM solutions by augmenting scarce multivariate time series data.
problem Data scarcity in failure prognostics.
method Extends autoregressive models for data augmentation.
result Significantly improves PHM solution performance.
A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signalsstat.ML
A federated model predicts failures using multi-stream incomplete data.
problem Insufficient data for reliable prognostic models in multi-stream applications.
method Federated data fusion, multivariate functional principal component analysis, (log)-location-scale regression model, federated algorithm.
result Performance is as good as classic non-federated models and better than individual models.
Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic scorestat.ML
Improves trial efficiency by adjusting for historical prognostic scores.
problem Reducing statistical uncertainty in randomized trial estimates.
method Linear covariate adjustment using a prognostic model trained on historical data.
result Prognostic covariate adjustment achieves minimum variance and reduces mean-squared error.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
problem Limited run-to-failure data samples for accurate RUL prognosis.
method Federated learning framework, decentralized validation, and robust aggregation methods.
result The federated learning framework leads to more accurate RUL prognosis for five out of six airlines.
Paper proposes a tensor data model for incomplete imaging data.
problem Prognostics models for incomplete imaging data.
method Supervised tensor dimension reduction with TTF supervision and optimization.
result Model effectively extracts low-dimensional features from incomplete data.
Prognostic scores improve logistic regression analysis in RCTs with binary outcomes.
problem Non-collapsibility in logistic regression analysis of RCTs with binary endpoints.
method Prognostic score adjustment using AI predictions to address non-collapsibility.
result Prognostic score adjustment increases power or reduces sample size for estimating conditional odds ratios.
G-computation improves clinical trial power with machine learning.
problem Balancing prognostic factors in randomized trials to prevent near-confounders.
method G-computation with penalized models (Lasso, Elasticnet) and algorithm-based methods (neural network, SVM, super learner).
result G-computation with Elasticnet and splines reduces variance and increases power in RCTs.
Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Predictionstat.ML
The paper proposes a method to estimate heterogeneous treatment effects using pretraining strategies.
problem Estimating conditional average treatment effects (CATE) in the presence of many covariates.
method The approach leverages prognostic factors that also predict treatment effect heterogeneity, using the R-learner framework.
result The proposed method improves estimation accuracy and power for detecting treatment effect heterogeneity.
CBMs improve interpretability in RUL prediction for aircraft engines.
problem Lack of interpretability in deep learning models for asset prognostics.
method Concept Bottleneck Models (CBMs) for RUL prediction.
result CBMs achieve comparable or superior performance to black-box models while being more interpretable.
Proposes a federated learning approach for industrial asset failure prediction.
problem Lack of data and privacy concerns in industrial prognostics.
method Two-stage federated learning: dimension reduction and parameter estimation.
result Validated the approach using simulated and real data.
This paper improves federated learning for industrial predictive analytics by accommodating client heterogeneity.
problem Traditional federated models assume homogeneity in degradation processes, which doesn't apply to industrial settings.
method Personalized federated prognostic model using proximal gradient descent algorithm for joint parameter estimation.
result The proposed model enhances performance and provides comprehensive failure time distributions.
A new VAE model identifies and estimates treatment effects with limited overlap.
problem Identifying and estimating treatment effects when subjects with certain features belong to a single treatment group.
method Developed a latent variable model to estimate a prognostic score, which is sufficient for treatment effects. The model is a new type of VAE called β-Intact-VAE.
result The model identifies individualized treatment effects and provides TE error bounds.
Background: Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typica…
Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.
problem Limited labelled data for RUL prediction in fatigue damage prognosis.
method Pre-training deep learning models on unlabelled sensor data using self-supervised learning.
result Self-supervised pre-trained models significantly outperform non-pre-trained models in RUL prediction with scarce labelled data.
We study information theoretic methods for ranking biomarkers. In clinical trials there are two, closely related, types of biomarkers: predictive and prognostic, and disentangling them is a key challenge. Our first step is to phrase biomarker ranking in terms of optimizing an information theoretic quantity. This formal…
Proposes FMPCA for federated tensor data dimensionality reduction.
problem Integration of MPCA into federated learning.
method Federated Multilinear Principal Component Analysis (FMPCA).
result FMPCA preserves performance of traditional MPCA in federated learning.
Paper proposes ARPHMM for fault detection and prognosis in aero-engines.
problem Fault detection and prognosis in aero-engines using sensor data.
method Autoregressive Partially-hidden Markov Model (ARPHMM) with prior knowledge integration.
result Model estimates remaining useful life and degradation level.
We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis (ALS). We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, includ…
Transfer learning improves machine learning models for equipment diagnostics.
problem Limited model performance due to training data mismatch.
method Transfer learning to reuse knowledge from similar tasks.
result Transfer learning enhances model applicability in PHM.
Deep learning predicts AMD progression from longitudinal fundus images.
problem Predicting future stages of age-related macular degeneration (AMD).
method InceptionV3 feature vectors, interval scaling, Recurrent Neural Network.
result 0.878 sensitivity, 0.887 specificity, 0.950 AUC.
This paper studies an intelligent ultimate technique for health-monitoring and prognostic of common rotary machine components, particularly bearings. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse auto-encoder. Then, the correlation of the ex…
Paper presents a data preprocessing method for PHM models.
problem Lack of consistent data preprocessing for PHM applications.
method Comprehensive pipeline for sensor data preprocessing.
result Creation of clean data sets for training machinery health state classifiers.
Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modescs.LG
This research improves asset life prediction by integrating deep learning with mixture distributions.
problem Predicting residual useful life for assets with multiple failure modes.
method Integrates mixture (log)-location-scale distribution with deep learning.
result Proposed models outperform existing methods in predicting residual useful life.
The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment. However, they generally suffer from this problem of lacking prior knowledge to …
Bayesian Semi-nonnegative Tri-matrix Factorization to Identify Pathways Associated with Cancer Typesstat.ML
Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of variou…
We analyse an issue when comparing survival curves between two subgroups. We show that there is a direct relationship between estimates of subgroups' survival at a time point and positive and negative predictive values in the binary classification settings. Our findings present a case where current methods of comparing…
Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical…
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognosticscs.LG
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples. Synthetic oversampling methods are commonly used to tackle these problems b…
Study builds models to predict post-cardiac arrest outcomes using patient data.
problem Lack of accurate prognostication methods for patients resuscitated from cardiac arrest.
method Integrated electronic health records (EHR) and physiological time series (PTS) data to train machine learning classifiers.
result Combined EHR-PTS24 models outperformed models using either EHR or PTS24 alone in predicting survival and neurological outcomes.
Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modesstat.ML
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
problem Predicting remaining useful life of DSH subsystems with unknown failure modes.
method Unsupervised framework using mixture of Gaussian regressions and Expectation-Maximization algorithm.
result Improved prediction accuracy and interpretability of RUL.
Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. The intention of this study was to overview of a variety of performance metrics and approaches to their classification. The main goal of the study was to develop a typology that will help to improve our knowledge a…
Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios wher…
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
problem Estimating ATT marginal hazard-ratio in externally controlled survival trials
method Machine-learning-assisted generalized entropy calibration for IPW Cox regression
result Reduces bias, increases efficiency, and improves coverage
This study evaluates uncertainty quantification methods for deep learning in predictive maintenance.
problem Uncertainty quantification for reliable decision-making in predictive maintenance.
method State-of-the-art variational inference algorithms for Bayesian neural networks (BNN), Monte Carlo Dropout (MCD), deep ensembles (DE), and heteroscedastic neural networks (HNN) were tested.
result No method clearly outperforms others in all situations, but DE and MCD provide more conservative uncertainty estimates.
This paper reviews methods for discovering patient subgroups from EHR data.
problem Discovering subgroups of patients and co-occurring medical conditions from EHR data.
method Low-rank data approximation methods like matrix and tensor decompositions.
result These methods provide transparent and interpretable insights into patient phenotypes.
The paper uses SHAP for interpreting machine learning models in hospital data.
problem Interpreting machine learning models in healthcare.
method SHAP for feature importance and feature packing techniques.
result SHAP provides better interpretability of machine learning models in healthcare.
Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. Statistical predictors are not robust enough to correctly identify patients who would benefit from Transcatheter Aortic Valve Implantation (TAVI). This research aims to create a machine learning model…
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlapstat.ML
Deconfounding scores improve causal effect estimation with weak overlap.
problem Challenges in causal treatment effect estimation due to weak overlap in high-dimensional data.
method Propose deconfounding scores to preserve identification and target estimation while improving overlap.
result Prognostic scores are overlap-optimal under a broad family of generalized linear models with Gaussian features.
New algorithm forecasts health indicators for better equipment lifespan prediction.
problem Improving equipment lifespan prediction through health indicator forecasting.
method Generative + scenario matching approach using Gaussian Process.
result Superior performance compared to existing methods.
Bayesian PROCOVA uses AI to adjust for covariates in RCTs.
problem Unbiased and precise treatment effect inferences from RCTs.
method Generative AI constructs digital twins for covariate adjustment, using an additive mixture prior.
result Efficiency gains in smaller RCTs compared to frequentist methods.
As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure…
New method interprets deep learning for causal effects, separating prognostic and moderating covariates.
problem Estimating individual causal/treatment effects under confounders.
method Deep counterfactual learning architecture for estimating CATE with interpretable score functions.
result Demonstrated improved interpretability and quantification of uncertainty in CATE estimation.