Application of change point analysis to daily influenza-like illness emergency department visits

Background: The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends. Objective: To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI. Methodology and principal findings Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when −0.2≤ρ≤0.2 and 80% when −0.5≤ρ≤0.5). During the 2008–9 season, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009–10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states. Conclusions and significance As a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems

Wearables in medicine

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches

Penalized method based on representatives and nonparametric analysis of gap data

When there are a large number of predictors and few observations, building a regression model to explain the behavior of a response variable such as a patient's medical condition is very challenging. This is a "p ≫n " variable selection problem encountered often in modern applied statistics and data mining. Chapter one of this thesis proposes a rigorous procedure which groups predictors into clusters of "highly-correlated" variables, selects a representative from each cluster, and uses a subset of the representatives for regression modeling. The proposed Penalized method based on Representatives (PR) extends the Lasso for the p ≫ n data and highly correlated variables, to build a sparse model practically interpretable and maintain prediction quality. Moreover, we provide the PR-Sequential Grouped Regression (PR-SGR) to make computation of the PR procedure efficient. Simulation studies show the proposed method outperforms existing methods such as the Lasso/Lars. A real-life example from a mental health diagnosis illustrates the applicability of the PR-SGR. In the second part of the thesis, we study the analysis of time-to-event data called a gap data when missing time intervals (gaps) possibly happen prior to the first observed event time. If a gap occurs prior to the first observed event, then the first observed event may or may not be the first true event. This incomplete knowledge makes the gap data different from the well-studied regular interval censored data. We propose a Non-Parametric Estimate for the Gap data (NPEG) to estimate the survival function for the first true event time, derive its analytic properties and demonstrate its performance in simulations. We also extend the Imputed Empirical Estimating method (IEE), which is an existing nonparametric method for the gap data up to one gap, to handle the gap data with multiple gaps.Ph.D.Committee Chair: Lu, Jye-Chyi; Committee Member: Grover, Martha; Committee Member: Huo, Xiaoming; Committee Member: Mei, Yajun; Committee Member: Serban, Nicolet

A Study on Affective Dimensions to Engine Acceleration Sound Quality Using Acoustic Parameters

The technical performance of recent automobiles is highly progressed and standardized across different manufacturers. This study seeks to derive a semantic space of engine acceleration sound quality for end users and identify the relation with sound characteristics. For this study, two affective attributes: ‘refined’ and ‘powerful’, and eight acoustic parameters considering revolutions per minute were used to determine the correlation coefficient for those affective attributes. In the experiment, a total of 35 automobiles were selected. Each of the 3rd gear wide open throttle sounds was recorded and evaluated by 42 adult subjects with normal hearing ability and driving license. Their subjective evaluations were analyzed using factor analysis, independent t-test, correlation analysis, and regression analysis. The prediction models for the affective dimensions show distinct differences for the revolutions per minute. From the experiment, it was confirmed that the customers’ affective response can be predicted through the acoustic parameters. In addition, it was found that the initial revolutions per minute in the accelerated condition had the greatest influence on the affective response. This study can be a useful guideline to design engine acceleration sounds that satisfy customers’ affective experience

Active sound design development based on the harmonics of main order from engine sound

Most previous studies on active sound design (ASD) development proposed regression models based on psychoacoustic parameters for engine sound design. However, order-based parameters are required for a real ASD development, considering that an ASD system is controlled by order levels. In this paper, we propose a regression model utilizing order-level-based parameters that can be efficiently applied to ASD development. A jury test was conducted for 27 engine sound recordings using 36 participants with normal hearing ability to evaluate the level of affective adjectives. Then, acoustic parameters were measured from the engine sound recordings to identify the relationship between the adjectives and parameters. Finally, a regression model was derived through statistical analysis. The properties of the model were compared with those of models proposed in previous studies to verify its superiority. The proposed regression model can reduce the time and effort required for ASD development.N

Mental Illness and Co-morbid Conditions: BioSense 2008 - 2011

OBJECTIVE: The purpose of this paper was to analyze the associated burden of mental illness and medical comorbidity using BioSense data 2008–2011. INTRODUCTION: Understanding the relationship between mental illness and medical comorbidity is an important aspect of public health surveillance. In 2004, an estimated one fourth of the US adults reported having a mental illness in the previous year (1). Studies showed that mental illness exacerbates multiple chronic diseases like cardiovascular diseases, diabetes and asthma (2). BioSense is a national electronic public health surveillance system developed by the Centers for Disease Control and Prevention (CDC) that receives, analyzes and visualizes electronic health data from civilian hospital emergency departments (EDs), outpatient and inpatient facilities, Veteran Administration (VA) and Department of Defense (DoD) healthcare facilities. Although the system is designed for early detection and rapid assessment of all-hazards health events, BioSense can also be used to examine patterns of healthcare utilization. METHODS: We used 4 years (2008 – 2011) of BioSense civilian hospitals’ EDs visit data to perform the analysis. We searched final diagnoses for ICD-9 CM codes related to mental illness (290 – 312), schizophrenia (295), major depressive disorder (296.2 – 296.3), mood disorder (296, 300.4 and 311) and anxiety, stress & adjustment disorders (300.0, 300.2, 300.3, 308, and 309). We used BioSense syndromes/sub-syndromes based on chief complaints and final diagnoses for comorbidity. For the purpose of this study, comorbidity was defined broadly as the co-occurrence of mental and physical illness in the same person regardless of the chronological order. The proportion was calculated as the number of mental health visits associated with comorbidity divided by the total number of mental illness relevant visits. We ranked the top 10 proportions of comorbidity for adult mental illness by year. RESULTS: From 2008–2011, there were 4.6 million visits where mental illness was reported in the EDs visits. Average age of those reported mental illness was 44 years, 55% were women and 45% were men. More women were reported with anxiety (67%), mood (66%), and major depressive disorders (59%) than men; while men were reported more with schizophrenia (56%) than women (44%). The most common comorbid condition was hypertension, followed by chest pain, abdominal pain, diabetes, nausea & vomiting and dyspnea (Table 1). Ranks of injury, falls, headache and asthma were slightly variant by year. CONCLUSIONS: This study supports prior findings that adult mental illness is associated with substantial medical burden. We identified 10 most common comorbid condition associated with mental illness. The major limitation of this work was that electronic data does not allow determination of the causal pathway between mental illness and some medical comorbidity. In addition, data represents only those who have access to healthcare or those with health seeking behaviors. Familiarity with comorbid conditions affecting persons with adult mental illness may assist programs aimed at providing medical care for the mentally ill

Acousto-microfluidics for screening of ssDNA aptamer

We demonstrate a new screening method for obtaining a prostate-specific antigen (PSA) binding aptamer based on an acoustofluidic separation (acoustophoreis) technique. Since acoustophoresis provides simultaneous washing and separation in a continuous flow mode, we efficiently obtained a PSA binding aptamer that shows high affinity without any additional washing step, which is necessary in other screening methods. In addition, next-generation sequencing (NGS) was applied to accelerate the identification of the screened ssDNA pool, improving the selecting process of the aptamer candidate based on the frequency ranking of the sequences. After the 8 th round of the acoustophoretic systematic evolution of ligands by exponential enrichment (SELEX) and following sequence analysis with NGS, 7 PSA binding ssDNA aptamer-candidates were obtained and characterized with surface plasmon resonance (SPR) for affinity and specificity. As a result of the new SELEX method with PSA as the model target protein, the best PSA binding aptamer showed specific binding to PSA with a dissociation constant (K d) of 0.7 nM

Correction to: A longitudinal census of the bacterial community in raw milk correlated with Staphylococcus aureus clinical mastitis infections in dairy cattle

BACKGROUND: Staphylococcus aureus is a common cause of clinical mastitis (CM) in dairy cattle. Optimizing the bovine mammary gland microbiota to resist S. aureus colonization is a growing area of research. However, the details of the interbacterial interactions between S. aureus and commensal bacteria, which would be required to manipulate the microbiome to resist infection, are still unknown. This study aims to characterize changes in the bovine milk bacterial community before, during, and after S. aureus CM and to compare bacterial communities present in milk between infected and healthy quarters. METHODS: We collected quarter-level milk samples from 698 Holstein dairy cows over an entire lactation. A total of 11 quarters from 10 cows were affected by S. aureus CM and milk samples from these 10 cows (n = 583) regardless of health status were analyzed by performing 16S rRNA gene amplicon sequencing. RESULTS: The milk microbiota of healthy quarters was distinguishable from that of S. aureus CM quarters two weeks before CM diagnosis via visual inspection. Microbial network analysis showed that 11 OTUs had negative associations with OTU0001 (Staphylococcus). A low diversity or dysbiotic milk microbiome did not necessarily correlate with increased inflammation. Specifically, Staphylococcus xylosus, Staphylococcus epidermidis, and Aerococcus urinaeequi were each abundant in milk from the quarters with low levels of inflammation. CONCLUSION: Our results show that the udder microbiome is highly dynamic, yet a change in the abundance in certain bacteria can be a potential indicator of future S. aureus CM. This study has identified potential prophylactic bacterial species that could act as a barrier against S. aureus colonization and prevent future instances of S. aureus CM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42523-022-00211-x

Mental Illness and Co-morbid Conditions: BioSense 2008 - 2011

To analyze the associated burden of mental illness and medical comorbidity using BioSense data 2008-2011. Understanding the relationship between mental illness and medical comorbidity is an important aspect of public health surveillance