scholarly journals Identifying the best questions for rapid screening of secondhand smoke exposure among children

2020 ◽  
Author(s):  
Albert J Ksinan ◽  
Yaou Sheng ◽  
Elizabeth K Do ◽  
Julia C Schechter ◽  
Junfeng (Jim) Zhang ◽  
...  
Keyword(s):  
At Risk ◽  
Screening Tool ◽  
Smoke Exposure ◽  
Data Driven ◽  
The Past ◽  
Smoking Exposure ◽  
Data Driven Approach ◽  
Shs Exposure ◽  
Child Age ◽  
Cotinine Concentration

Abstract Introduction Many children suffer from secondhand smoke exposure (SHSe), which leads to a variety of negative health consequences. However, there is no consensus on how clinicians can best query parents for possible SHSe among children. We employed a data-driven approach to create an efficient screening tool for clinicians to quickly and correctly identify children at risk for SHSe. Methods Survey data from mothers and biospecimens from children were ascertained from the Neurodevelopment and Improving Children’s Health following Environmental Tobacco Smoke Exposure (NICHES) study. Included were mothers and their children whose saliva were assayed for cotinine (n = 351 pairs, mean child age = 5.6 years). Elastic net regression predicting SHSe, as indicated from cotinine concentration, was conducted on available smoking-related questions and cross-validated with 2015-2016 National Health and Nutrition Examination Survey (NHANES) data to select the most predictive items of SHSe among children (n = 1,670, mean child age = 8.4 years). Results Answering positively to at least one of the two final items (“During the past 30 days, did you smoke cigarettes at all?” and “Has anyone, including yourself, smoked tobacco in your home in the past 7 days?”) showed AUC = .82, and good specificity (.88) and sensitivity (.74). These results were validated with similar items in the nationally-representative NHANES sample, AUC = .82, specificity = .78, and sensitivity = .77. Conclusions Our data-driven approach identified and validated two items that may be useful as a screening tool for a speedy and accurate assessment of SHSe among children. Implications The current study used a rigorous data-driven approach to identify questions that could reliably predict secondhand smoking exposure (SHS) among children.Using saliva cotinine concentration levels as a gold standard for determining SHS exposure, our analysis employing elastic net regression identified two questions that served as good classifier for distinguishing children who might be at risk for SHS exposure. The two items that we validated in the current study can be readily used by clinicians, such as pediatricians, as part of screening procedures to quickly identify whether children might be at risk for secondhand smoking exposure.

scholarly journals A Data-Driven Approach to Development of a Taxonomy Framework for Triple Bottom Line Metrics

2019 ◽  
Vol 11 (9) ◽  
pp. 2717
Author(s):  
Fátima L. Vieira ◽  
Paulo A. Vieira ◽  
Denis A. Coelho
Keyword(s):  
Screening Tool ◽  
Triple Bottom Line ◽  
Computational Approach ◽  
Data Driven ◽  
Specific Context ◽  
Bottom Line ◽  
Conscious Decision ◽  
Data Driven Approach ◽  
The Subject ◽  
The One

This paper proposes a data-driven approach to develop a taxonomy in a data structure on list for triple bottom line (TBL) metrics. The approach is built from the authors reflection on the subject and review of the literature about TBL. The envisaged taxonomy framework grid to be developed through this approach will enable existing metrics to be classified, grouped, and standardized, as well as detect the need for further metrics development in uncovered domains and applications. The approach reported aims at developing a taxonomy structure that can be seen as a bi-dimensional table focusing on feature interrogations and characterizing answers, which will be the basis on which the taxonomy can then be developed. The interrogations column is designed as the stack of the TBL metrics features: What type of metric is it (qualitative, quantitative, or hybrid)? What is the level of complexity of the problems where it is used? What standards does it follow? How is the measurement made, and what are the techniques that it uses? In what kinds of problems, subjects, and domains is the metric used? How is the metric validated? What is the method used in its calculation? The column of characterizing answers results from a categorization of the range of types of answers to the feature interrogations. The approach reported in this paper is based on a screening tool that searches and analyzes information both within abstracts and full-text journal papers. The vision for this future taxonomy is that it will enable locating for any specific context, discern what TBL metrics are used in that context or similar contexts, or whether there is a lack of developed metrics. This meta knowledge will enable a conscious decision to be made between creating a new metric or using one of those that already exists. In this latter case, it would also make it possible to choose, among several metrics, the one that is most appropriate to the context at hand. In addition, this future framework will ease new future literature revisions, when these are viewed as updates of this envisaged taxonomy. This would allow creating a dynamic taxonomy for TBL metrics. This paper presents a computational approach to develop such taxonomy, and reports on the initial steps taken in that direction, by creating a taxonomy framework grid with a computational approach.

The Vanderbilt PICC Service: Program, Procedural, and Patient Outcomes Successes

2005 ◽  
Vol 10 (4) ◽  
pp. 183-192 ◽  
Author(s):  
Doug Burns
Keyword(s):  
Patient Outcomes ◽  
Medical Center ◽  
Nursing Program ◽  
Data Driven ◽  
Complication Rates ◽  
Advanced Practice ◽  
The Past ◽  
Data Driven Approach ◽  
High Level ◽  
Vanderbilt University

Abstract Since its inception in early 2000, Vanderbilt University's Peripherally Inserted Central Catheter (PICC) Service has experienced a high level of success as measured by high proficiency rates and increasing patient procedures each year, low complication rates during and after PICC placements, and an increasing scope of influence within the Vanderbilt University Medical Center and Children's Hospital, the surrounding community, and in the Southeastern United States. Primary drivers of the PICC Service's continuing success include consistent applications of technique and technology, a data-driven approach to assessing the program's progress, and appropriately managing customers' expectations and needs. Over the past five years, data were collected on more than 12,500 PICC placements performed in this specialized nursing program. Retrospective analyses of the data demonstrate an increasing rate of successful placements (from 87.2% to 92.4%) since the program's inception in 2000 to late 2004. Furthermore, the choice of PICC technology has had a significant impact on the odds for occlusion or infection. The Vanderbilt PICC Service provides a model by which other programs can be established, maintained, and expanded into advanced practice.

scholarly journals Assessing secondhand and thirdhand tobacco smoke exposure in Canadian infants using questionnaires, biomarkers, and machine learning

2021 ◽  
Author(s):  
Jaclyn Parks ◽  
Kathleen E. McLean ◽  
Lawrence McCandless ◽  
Russell J. de Souza ◽  
Jeffrey R. Brook ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tobacco Smoke ◽  
Smoke Exposure ◽  
Tobacco Smoke Exposure ◽  
Linear Regression Models ◽  
Thirdhand Smoke ◽  
Urinary Cotinine ◽  
Smoking Exposure ◽  
Shs Exposure ◽  
Technological Improvements

Abstract Background As smoking prevalence has decreased in Canada, particularly during pregnancy and around children, and technological improvements have lowered detection limits, the use of traditional tobacco smoke biomarkers in infant populations requires re-evaluation. Objective We evaluated concentrations of urinary nicotine biomarkers, cotinine and trans-3’-hydroxycotinine (3HC), and questionnaire responses. We used machine learning and prediction modeling to understand sources of tobacco smoke exposure for infants from the CHILD Cohort Study. Methods Multivariable linear regression models, chosen through a combination of conceptual and data-driven strategies including random forest regression, assessed the ability of questionnaires to predict variation in urinary cotinine and 3HC concentrations of 2017 3-month-old infants. Results Although only 2% of mothers reported smoking prior to and throughout their pregnancy, cotinine and 3HC were detected in 76 and 89% of the infants’ urine (n = 2017). Questionnaire-based models explained 31 and 41% of the variance in cotinine and 3HC levels, respectively. Observed concentrations suggest 0.25 and 0.50 ng/mL as cut-points in cotinine and 3HC to characterize SHS exposure. This cut-point suggests that 23.5% of infants had moderate or regular smoke exposure. Significance Though most people make efforts to reduce exposure to their infants, parents do not appear to consider the pervasiveness and persistence of secondhand and thirdhand smoke. More than half of the variation in urinary cotinine and 3HC in infants could not be predicted with modeling. The pervasiveness of thirdhand smoke, the potential for dermal and oral routes of nicotine exposure, along with changes in public perceptions of smoking exposure and risk warrant further exploration.

Failure Prognostics for In-Tank Fuel Pumps of the Returnless Fuel Systems

2016 ◽  
Author(s):  
Ehsan Taheri ◽  
Oleg Gusikhin ◽  
Ilya Kolmanovsky
Keyword(s):  
Electrical Signal ◽  
Data Driven ◽  
Connected Vehicle ◽  
Reliability Models ◽  
The Past ◽  
Degradation Modeling ◽  
Fuel Delivery ◽  
Data Driven Approach ◽  
Alternative Approaches ◽  
Type Fuel

With the motivation to develop Condition Based Maintenance (CBM) strategies for the automotive vehicles, this paper considers a data-driven approach to the prognostics of the automotive fuel pumps. Focusing on the returnless type fuel delivery systems, our approach is based on estimating the fuel pump workload based on the model learned from the past driving history. Statistical reliability models are then exploited to estimate failure probability. These models are formulated in terms of the workload and updated from data available from vehicles in the field. Numerical examples which illustrate the proposed methodology are reported. Compared to alternative approaches, which are based on detailed physics-based degradation modeling and/or electrical signal analysis, our approach is data-driven, exploits connected vehicle analytics and reliability-based modeling, and has a potential to lead to simpler implementations.

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities

2021 ◽  
Vol 61 (1) ◽  
pp. 159-179 ◽  
Author(s):  
Saad Khan ◽  
Ruth Hauptman ◽  
Libusha Kelly
Keyword(s):  
Radiation Therapy ◽  
Adverse Events ◽  
Data Driven ◽  
Medical Interventions ◽  
The Past ◽  
Challenges And Opportunities ◽  
Microbiome Research ◽  
Data Driven Approach ◽  
Difficult Challenge ◽  
The Many

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

scholarly journals A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

2021 ◽  
Vol 1 ◽  
Author(s):  
Constantin Falk ◽  
Ron Van de Sand ◽  
Sandra Corasaniti ◽  
Jörg Reiff-Stephan
Keyword(s):  
Operating Conditions ◽  
Data Driven ◽  
Sensor Data ◽  
Comparison Study ◽  
True Negative ◽  
The Past ◽  
Use Of Data ◽  
Data Driven Approach ◽  
System States ◽  
System Operating

Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.

scholarly journals A data-driven framework for paleomagnetic Euler pole analysis

2021 ◽  
Author(s):  
Leandro Gallo ◽  
Facundo Sapienza ◽  
Mathew Domeier
Keyword(s):  
Synthetic Data ◽  
Data Driven ◽  
Web Browser ◽  
Deep Time ◽  
Polar Wander ◽  
The Past ◽  
Apparent Polar Wander Path ◽  
Euler Pole ◽  
Data Driven Approach ◽  
Pole Analysis

Owing to the inherent axial symmetry of the Earth’s magnetic field, paleomagnetic data only directly record the latitudinal and azimuthal positions of crustal blocks in the past, but paleolongitude cannot be constrained. An ability to overcome this obstacle is fundamental to paleogeographic reconstruction. The paleomagnetic Euler pole (PEP) analysis presents a unique means to recover such information in deep-time. However, prior applications of the PEP method have invariably incorporated subjective decisions into its execution, undercutting its fidelity and rigor. Here we present a data-driven approach to PEP analysis that addresses some of these deficiencies---namely the objective identification of change-points and small-circle arcs that together approximate an apparent polar wander path. We elaborate on our novel methodology and conduct some experiments with synthetic data to demonstrate its performance. We furthermore present implementations of our methods both as adaptable, stand-alone scripts and as a streamlined interactive workflow that can be operated through a web browser.

scholarly journals A data-driven approach for assessing biking safety in cities

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sara Daraei ◽  
Konstantinos Pelechrinis ◽  
Daniele Quercia
Keyword(s):  
Local Governments ◽  
Policy Decision ◽  
Sustainable Transportation ◽  
Data Driven ◽  
Critical Factors ◽  
The Past ◽  
The World ◽  
Accident Data ◽  
Data Driven Approach ◽  
Policy Decision Making

AbstractWith the focus that cities around the world have put on sustainable transportation during the past few years, biking has become one of the foci for local governments globally. Cities all over the world invest in biking infrastructure, including bike lanes, bike parking racks, shared (dockless) bike systems etc. However, one of the critical factors in converting city-dwellers to (regular) bike users/commuters is safety. In this work, we utilize bike accident data from different cities to model the biking safety based on street-level (geographical and infrastructural) features. Our evaluations indicate that our model provides well-calibrated probabilities that accurately capture the risk of a biking accident. We further perform cross-city comparisons in order to explore whether there are universal features that relate to cycling safety. Finally, we discuss and showcase how our model can be utilized to explore “what-if” scenarios and facilitate policy decision making.

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