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Publications (41)
Background:
Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD.
Objective:
The aim is to examine patient enga...
Background:
Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predi...
BACKGROUND
Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD.
OBJECTIVE
The aim is to examine patient engageme...
Introduction
Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substan...
Context
Rapid growth has been suggested to promote islet autoimmunity and progression to type 1 diabetes. Childhood growth has not been analyzed separately from infant growth period in most previous studies, which may have distinct features due to differences between those stages of development.
Objective
We aimed to analyze the association of chi...
This study investigates a missing value imputation approach for longitudinal growth data in pediatric studies from multiple countries. We analyzed a combined cohort from five natural history studies of type 1 diabetes (T1D) in the US and EU with longitudinal growth measurements for 23,201 subjects. We developed a multiple imputation methodology usi...
Using real-world data from the Academy of Nutrition and Dietetics Health Informatics Infrastructure, we use state-of-the-art clustering techniques to identify 2 phenotypes characterizing the episodes of nutrition care observed in the National Quality Improvement (NQI) registry data set. The 2 phenotypes identified from recorded Nutrition Care Proce...
We explored the association between growth features and T1D development using landmark analysis at different ages. Analysis included individuals from 2 birth cohort studies: DAISY and BABYDIAB (n=2,664; 129 progressed to T1D). Using height and weight measured over time, percentiles for age were calculated. Missing values were imputed using LMS para...
Railcar asymmetric wheel wear leads to severe wear on one wheel but mild wear on the other wheel. The consequences of the asymmetric wheel include accelerated wear, mechanical failure and downtime, and high financial penalties. Therefore, identifying the asymmetric wheel wear is critical not only for cost effective maintenance but also for safe ope...
BACKGROUND
Technology-enabled ecological momentary assessment (EMA) facilitates the calibration of physiological signals against self-reported data and contexts. However, research using this method rarely considers the impact that user experience (UX) has on the quality of data.
OBJECTIVE
The purpose of this study is to explore the biases that UX...
In recent years, there has been growing interest in the use of fitness trackers and smartphone applications for promoting physical activity. Most of these applications use accelerometers to measure the level of activity that users engage in and provide descriptive, interactive reports of a user's step counts. While these reports are data-driven and...
Train wheel failures account for disruptions of train operations and even a large portion of train derailments. Remaining useful life (RUL) of a wheelset measures how soon the next failure will arrive, and the failure type reveals how severe the failure will be. RUL prediction is a regression task, whereas failure type is a classification task. In...
Though many applications involve autocorrelated multivariate counts, there is a scarcity of research on statistical modeling of them. To fill this research gap, this paper proposes a state space model to describe autocorrelated multivariate counts. The model builds upon the multivariate log-normal mixture Poisson distribution and allows for serial...
Electric utilities spend a large amount of their resources and budget on managing unplanned outages, the majority of which are driven by weather. The weather is the largest contributing factor for power outages faced by the population in the United States and several other countries. A major ongoing effort by utilities is to improve their emergency...
Nowadays, railway networks are instrumented with various wayside detectors. Such detectors, automatically identifying potential railcar component failures, are able to reduce rolling stock inspection and maintenance costs and improve railway safety. In this paper, we present a methodology to predict remaining useful life (RUL) of both wheels and tr...
Developing prognostics and health management (PHM) approaches for lithium-ion batteries has received increasing attention in recent years. This paper presents a new modeling framework to characterize lithium-ion battery degradation by examining detailed discharging voltage profiles in different discharging cycles. We propose a hierarchical model, c...
A major ongoing effort by utilities is to improve their emergency preparedness process for weather events, in order to: 1) reduce outage time 2) reduce repair and restoration costs and 3) improve customer satisfaction. This paper proposes a method for forecasting the number of damages of different types that will result from a weather event, up to...
Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered...
Nowadays railway networks are instrumented with various wayside detectors. Given massive amount of data collected from electronic wayside detectors, railcar failure prediction has recently attracted great attention in order to reduce rolling stock inspection and maintenance costs and improve railway safety. In this work, we present a methodology to...
A method and system for accurately predicting the remaining useful life of devices and components based on rigorous statistical analysis data to reduce service costs by implementing condition-based maintenance. One rigorous statistical model is the general degradation path model, which can be used to generate simulated data that shares similar data...
Right-censored failure time data is a common data type in manufacturing industry and healthcare applications. Some control charting procedures were previously proposed to monitor the right-censored failure time data under some specific distributional assumptions for the observed failure times and censoring times. But these assumptions may not be al...
Process trace data (PTD) is an important data type in semiconductor manufacturing and has a very large aggregate volume. While data mining and statistical analysis play a key role in the quality control of wafers, the existence of outliers adversely affects the applications benefiting from PTD analysis. Due to the complexities of PTD and the result...
A method for generating service rules corresponding to business data is disclosed. A plurality of business related data is gathered from various sources. The data is combined using a subjective logic technique. The data is then evaluated for temporal patterns. Finally a set of service rules corresponding to the combined business data are developed.
Process trace data (PTD) is an important data type in semiconductor manufacturing and consists of a huge amount of time series data collected from many different sensors during all manufacturing processing steps. PTD contains abundant information about the tool status and thus can be used for improving tool stability and tool matching. For this aim...
A common type of reliability data is the right censored time-to-failure data. In this article, we developed a control chart to monitor the time-to-failure data in the presence of right censoring using weighted rank tests. On the basis of the asymptotic properties of the rank statistics, we derived the generic formulae for the operating characterist...
Condition based maintenance (CBM) is an important maintenance strategy in practice. In this paper, we propose a CBM method to effectively incorporate system health observations into maintenance decision making to minimise the total maintenance cost and cost variability. In this method, the system degradation process is described by a Cox PH model a...
To enhance the reliability and availability of complex engineering products and reduce the service costs, nowadays some efficient maintenance programs including Condition-based Maintenance (CBM) are implemented for remote diagnostics and prognostics in industries. Prediction of Remaining Useful Life (RUL) for the unit/part is a key aspect of progno...
The Proportional Hazards (PH) model is an important type of failure time regression model which relates the occurrence probability of critical failures to influential factors. However, little research work has been done on detecting changes in the PH models fitted based on different sets of reliability data. This paper develops the methods for chan...
Statistical process monitoring (SPC) plays a very important role in manufacturing quality control. This paper addresses the statistical detection of both process faults and sensor faults. In many manufacturing processes and particularly in autobody assembly processes, the statistical distributions of the process quality characteristics and the sens...
The analysis of event sequence data that contains system failures is becoming increasingly important in the design of service and maintenance policies. This paper presents a systematic methodology to construct a statistical prediction model for failure event based on event sequence data. First, frequent failure signatures, defined as a group of eve...
Variation-source identification has received considerable attention from the manufacturing quality improvement community. One widely used method is based on a pattern matching procedure, which identifies process faults by comparing the fault symptom, which is the principal eigenvector of the covariance matrix of the quality measurement, with fault...
Variation source identification is a critical step in the quality and productivity improve- ment of manufacturing processes and draws significant attention recently. In this article we present a robust pattern-matching technique for variation source identification. In this paper, a multiple variation sources identification technique is developed by...