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.
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.
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.
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 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.
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.
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.
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.
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.
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…
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.
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.
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.
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.
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.
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.
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…
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…
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.
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.
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…
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…
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 …
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…
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…
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.
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.
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…
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…
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
The identification of predictive biomarkers from a large scale of covariates for subgroup analysis has attracted fundamental attention in medical research. In this article, we propose a generalized penalized regression method with a novel penalty function, for enforcing the hierarchy structure between the prognostic an…
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.
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 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.
Proposes a hierarchical model for learning discrete Bayesian networks with shrinkage.
problem Learning discrete Bayesian networks with high-order interactions and cell probabilities.
method Hierarchical Dirichlet shrinkage model with Metropolis-adjusted Langevin algorithm for sampling.
result Efficiently learns graph structure and selects between DAGs from sparse count data.
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…
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 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.
Prognosticator improves performance in non-stationary MDPs.
problem Real-world applications often violate the stationary assumption in reinforcement learning.
method A policy gradient algorithm that maximizes a forecast of future performance.
result Prognosticator outperforms online adaptation techniques in non-stationary MDPs.
Augmentation improves machine learning model performance on small datasets.
problem Suboptimal generalization performance of machine learning models on small datasets.
method Data augmentation to increase sample size and diversity.
result Augmentation improves AUC by 15.55% on average for small datasets.
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.
Proposes modifications to model-based forests for HTE estimation in observational data.
problem Estimating heterogeneous treatment effects in observational studies with complex outcomes.
method Orthogonalization strategy from Robinson (1988) applied to model-based forests.
result The orthogonalization strategy reduces confounding effects in simulated studies.
A new method improves treatment effect inferences in RCTs by adjusting for covariates and heteroskedasticity.
problem Improving treatment effect inferences in RCTs with efficient and powerful methods.
method Weighted Prognostic Covariate Adjustment Method (Weighted PROCOVA) for heteroskedasticity.
result The method reduces variance, maintains Type I error rate, and increases test power for treatment effect.
Quantitative CT predicts ILD patterns and prognosis.
problem Diagnosing and predicting prognosis of fibrosing ILD patterns.
method High-resolution CT texture features, TM model for classification and survival analysis.
result TM model outperforms histogram-based model in distinguishing UIP from non-UIP patterns and allows for survival group partitioning.