70 research outputs found
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data
This paper proposes a supervised dimension reduction methodology for tensor
data which has two advantages over most image-based prognostic models. First,
the model does not require tensor data to be complete which expands its
application to incomplete data. Second, it utilizes time-to-failure (TTF) to
supervise the extraction of low-dimensional features which makes the extracted
features more effective for the subsequent prognostic. Besides, an optimization
algorithm is proposed for parameter estimation and closed-form solutions are
derived under certain distributions.Comment: 42 pages, 17 figure
Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior
Tensor regression methods have been widely used to predict a scalar response
from covariates in the form of a multiway array. In many applications, the
regions of tensor covariates used for prediction are often spatially connected
with unknown shapes and discontinuous jumps on the boundaries. Moreover, the
relationship between the response and the tensor covariates can be nonlinear.
In this article, we develop a nonlinear Bayesian tensor additive regression
model to accommodate such spatial structure. A functional fused elastic net
prior is proposed over the additive component functions to comprehensively
model the nonlinearity and spatial smoothness, detect the discontinuous jumps,
and simultaneously identify the active regions. The great flexibility and
interpretability of the proposed method against the alternatives are
demonstrated by a simulation study and an analysis on facial feature data
Sparse reduced-rank regression for imaging genetics studies: models and applications
We present a novel statistical technique; the sparse reduced rank regression (sRRR) model
which is a strategy for multivariate modelling of high-dimensional imaging responses and
genetic predictors. By adopting penalisation techniques, the model is able to enforce sparsity
in the regression coefficients, identifying subsets of genetic markers that best explain
the variability observed in subsets of the phenotypes. To properly exploit the rich structure
present in each of the imaging and genetics domains, we additionally propose the use of
several structured penalties within the sRRR model. Using simulation procedures that accurately
reflect realistic imaging genetics data, we present detailed evaluations of the sRRR
method in comparison with the more traditional univariate linear modelling approach. In
all settings considered, we show that sRRR possesses better power to detect the deleterious
genetic variants. Moreover, using a simple genetic model, we demonstrate the potential
benefits, in terms of statistical power, of carrying out voxel-wise searches as opposed to
extracting averages over regions of interest in the brain. Since this entails the use of phenotypic
vectors of enormous dimensionality, we suggest the use of a sparse classification
model as a de-noising step, prior to the imaging genetics study. Finally, we present the
application of a data re-sampling technique within the sRRR model for model selection.
Using this approach we are able to rank the genetic markers in order of importance of association
to the phenotypes, and similarly rank the phenotypes in order of importance to
the genetic markers. In the very end, we illustrate the application perspective of the proposed
statistical models in three real imaging genetics datasets and highlight some potential
associations
Hybrid Statistical and Deep Learning Models for Diagnosis and Prognosis in Manufacturing Systems
In today’s highly competitive business environment, every company seeks to work at their full potential to keep up with competitors and stay in the market. Manager and engineers, therefore, constantly try to develop technologies to improve their product quality. Advancements in online sensing technologies and communication networks have reshaped the competitive landscape of manufacturing
systems, leading to exponential growth of Condition Monitoring (CM) data. High-dimensional data sources can be particularly important in process monitoring and their efficient utilization can help systems reach high accuracy in fault diagnosis and prognosis. While researches in Statistical Process Control (SPC) tools and Condition-Based Maintenance (CBM) are tremendous, their applications
considering high-dimensional data sets has received less attention due to the complexity and challenging nature of such data and its analysis. This thesis adds to this field by designing a
Deep Learning (DL) based survival analysis model towards CBM in the prognostic context and a DL and SPC based hybrid model for process diagnosis, both using high dimensional data. In the
first part, we a design support system for maintenance decision making by considering degradation signals obtained from CM data. The decision support system in place can predict system’s failure
probability in a smart way. In the second part, a Fast Region-based Convolutional Network (Fast R-CNN) model is applied to monitor the video input data. Then, specific statistical features are derived from the resulting bounding boxes and plotted on the multivariate Exponentially Weighted Moving Average (EWMA) control chart to monitor the process
Data Fusion and Systems Engineering Approaches for Quality and Performance Improvement of Health Care Systems: From Diagnosis to Care to System-level Decision-making
abstract: Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making.
The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine.
The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes.
The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
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