scholarly journals Sewer Pipes Condition Prediction Models: A State-of-the-Art Review

2019 ◽  
Vol 4 (4) ◽  
pp. 64 ◽  
Author(s):  
Malek Mohammadi ◽  
Najafi ◽  
Kaushal ◽  
Serajiantehrani ◽  
Salehabadi ◽  
...  
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
State Of The Art ◽  
Cost Effective ◽  
Linear Regression Models ◽  
Sewer Pipes ◽  
Logistic Regression Models ◽  
Social Environmental ◽  
Utility Managers ◽  
Future Condition

Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models.

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

scholarly journals Mexican Stock Exchange Performance after the Crisis of 2008: Application of Data Mining

2020 ◽  
Vol 18 ((1)) ◽  
Author(s):  
Eliseo Ramírez Reyes ◽  
Arturo Morales Castro ◽  
Néstor Juan Sanabria Landazábal
Keyword(s):  
Neural Networks ◽  
Default Risk ◽  
Regression Models ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sovereign Default ◽  
Linear Regression Models ◽  
Exchange Performance ◽  
Sovereign Default Risk ◽  
Dow Jones Industrial Average

Different prediction models are explored to analyze the performance of the Mexican Stock Exchange (PQI) after the 2008 crisis. These models have demonstrated a good prognostic capacity for both multivariable and univariable approaches given their non-parametric characteristics. The selected variables were: Dow Jones Industrial Average Index (DJIA), CPI, International Reserves (IR), CETES28, USDMX exchange rate, (M1) and the sovereign default risk of Mexico (MRDS). The models were evaluated with MAPE and compared with linear regression models (LR) and neural networks (NN). The results show that the models have a similar performance according to the percentages of error they presented.

scholarly journals Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables

2020 ◽  
Vol 13 (9) ◽  
pp. 189 ◽  
Author(s):  
Ahmed Ibrahim ◽  
Rasha Kashef ◽  
Menglu Li ◽  
Esteban Valencia ◽  
Eric Huang
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Vector Autoregression ◽  
State Of The Art ◽  
Bayesian Regression ◽  
Major Question ◽  
Factors Of Influence ◽  
Vector Auto Regression ◽  
Auto Regression ◽  
Price Discovery Process

The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models.

scholarly journals Ambient air pollution exposure and risk and progression of interstitial lung abnormalities: the Framingham Heart Study

Thorax ◽  
2019 ◽  
Vol 74 (11) ◽  
pp. 1063-1069 ◽  
Author(s):  
Mary B Rice ◽  
Wenyuan Li ◽  
Joel Schwartz ◽  
Qian Di ◽  
Itai Kloog ◽  
...  
Keyword(s):  
Air Pollution ◽  
Logistic Regression ◽  
Regression Models ◽  
Cardiac Ct ◽  
Prediction Models ◽  
Ambient Air ◽  
Chest Ct ◽  
Ambient Air Pollution ◽  
Logistic Regression Models ◽  
Lung Abnormalities

BackgroundAmbient air pollution accelerates lung function decline among adults, however, there are limited data about its role in the development and progression of early stages of interstitial lung disease.AimsTo evaluate associations of long-term exposure to traffic and ambient pollutants with odds of interstitial lung abnormalities (ILA) and progression of ILA on repeated imaging.MethodsWe ascertained ILA on chest CT obtained from 2618 Framingham participants from 2008 to 2011. Among 1846 participants who also completed a cardiac CT from 2002 to 2005, we determined interval ILA progression. We assigned distance from home address to major roadway, and the 5-year average of fine particulate matter (PM2.5), elemental carbon (EC, a traffic-related PM2.5 constituent) and ozone using spatio-temporal prediction models. Logistic regression models were adjusted for age, sex, body mass index, smoking status, packyears of smoking, household tobacco exposure, neighbourhood household value, primary occupation, cohort and date.ResultsAmong 2618 participants with a chest CT, 176 (6.7%) had ILA, 1361 (52.0%) had no ILA, and the remainder were indeterminate. Among 1846 with a preceding cardiac CT, 118 (6.4%) had ILA with interval progression. In adjusted logistic regression models, an IQR difference in 5-year EC exposure of 0.14 µg/m3 was associated with a 1.27 (95% CI 1.04 to 1.55) times greater odds of ILA, and a 1.33 (95% CI 1.00 to 1.76) times greater odds of ILA progression. PM2.5 and O3 were not associated with ILA or ILA progression.ConclusionsExposure to EC may increase risk of progressive ILA, however, associations with other measures of ambient pollution were inconclusive.

Prevalence and adverse impact of extended antibiotic prophylaxis in urologic oncology surgery.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 294-294
Author(s):  
Matthew Mossanen ◽  
Josh Calvert ◽  
Sarah Holt ◽  
Andrew Callaway James ◽  
Jonathan L. Wright ◽  
...  
Keyword(s):  
Antibiotic Prophylaxis ◽  
Regression Models ◽  
Linear Regression Models ◽  
Urologic Oncology ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Provider Characteristics ◽  
Diagnosis Codes ◽  
Postoperative Length ◽  
Hospital Acquired

294 Background: Providers exhibit variation in the selection of the class, dose, and duration of prescribed antibiotic prophylaxis (ABP) to prevent postsurgical infections. We sought to evaluate ABP practice patterns for common inpatient urologic oncology surgeries and ascertain the association between extended ABP and hospital-acquired Clostridium difficile (C. diff) infections. Methods: From the PREMIER database for 2007–2012, we identified patients who underwent radical prostatectomy (RP), radical or partial nephrectomy (Nephx), or radical cystectomy (RC). We defined extended ABP from charges for antibiotics ≥ 2 days after surgery; exclusive of patients with a switch in antibiotic class within 2 postoperative days for presumption of infection. We identified postoperative C. diff infections using ICD-9 diagnosis codes. Hierarchical linear regression models were constructed by procedure to identify patient and provider factors associated with extended ABP. Logistic regression models evaluated the association between extended ABP and postoperative C. diff infection, adjusting for patient and provider characteristics. Results: We identified 59,184 RP patients, 27,921 Nephx patients, and 5,425 RC patients. RC patients were more likely to receive extended ABP (56%) than RP (18%) or Nephx (29%) patients (p<0.001). Other factors associated with extended ABP included prolonged postoperative length of stay (OR ≥ 1.69, p<0.001 for all procedures), and surgical volume (p<0.001 for highest vs. lowest volume quartiles). Hospital identity explained 35% of the variability in ABP after RP, 23% after Nephx, and 20% after RC. Among Nephx and RC patients, extended ABP was associated with significantly higher odds of postoperative C. diff infection (OR 3.79, 95% CI 2.46–5.84, and OR 1.64, 95% CI 1.12–2.39, respectively). Conclusions: We identified marked hospital-level variability in extended ABP following RP, Nephx, and RC, which was associated with significantly increased odds of hospital-acquired C. diff infections. Efforts to increase provider compliance with national ABP guidelines may decrease preventable hospital-acquired infections after urologic cancer surgery.

scholarly journals Forgiveness of dolutegravir-based triple therapy compared to older antiretroviral regimens: a prospective multicenter cohort of adherence patterns and HIV-RNA replication

2021 ◽  
Author(s):  
Jean-Jacques Parienti ◽  
Anna L Fournier ◽  
Laurent Cotte ◽  
Marie-Paule Schneider ◽  
Manuel Etienne ◽  
...  
Keyword(s):  
Regression Models ◽  
Treatment Interruption ◽  
People Living With Hiv ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
Treatment Groups ◽  
Hiv Rna ◽  
Adherence Pattern ◽  
Living With Hiv ◽  
Rna Levels

Abstract Background For many people living with HIV (PLWH), taking antiretroviral therapy (ARV) every day is difficult. Methods Average adherence (Av-Adh) and log-transformed treatment interruption (TI) to ARV were prospectively measured over 6 months using electronic drug monitoring (EDM) in several cohorts of PLWH. Multivariate linear regression models including baseline confounders explored the influence of EDM-defined adherence (R 2) on 6-month Log10 HIV-RNA. Multivariate logistic regression models were used to compare the risk of HIV-RNA detection within subgroups stratified by lower (≤95%) and higher (&gt;95%) Av-Adh. Results Three hundred ninety nine PLWH were analyzed with different ARV: dolutegravir (n=102), raltegravir (n=90), boosted PI (bPI; n=107), and NNRTI (n=100). In the dolutegravir group, the influence of adherence pattern measures on R 2 for HIV-RNA levels was marginal (+2%). Av-Adh, TI and Av-Adh x TI increased the R 2 for HIV-RNA levels by 54% and 40% in the raltegravir and bPI treatment groups, respectively. TI increased the R 2 for HIV-RNA levels by 36% in the NNRTI treatment group. Compared to dolutegravir-based regimen, the risk of VR was significantly increased for: raltegravir (adjusted OR (aOR), 45.6; 95% confidence interval (CI) [4.5 - 462.1], p=0.001); NNRTIs (aOR, 24.8; 95% CI [2.7 - 228.4], p=0.005) and bPIs (aOR, 28.3; 95%CI [3.4 - 239.4], p=0.002) in PLWH with Av-Adh ≤95%. Among PLWH with &gt;95% Av-Adh, there were no significant differences on the risk of VR among the different ARV. Conclusion These findings support the concept that dolutegravir in combination with two other active ARVs achieves a greater virological suppression than older ARV, including raltegravir, NNRTI and bPI among PLWH with lower adherence.

scholarly journals Mathematical Regression Models for Prediction of Durability Properties of Foamed Concrete with the Inclusion of Coir Fibre

2019 ◽  
Vol 8 (6) ◽  
pp. 3353-3358
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Pulse Velocity ◽  
Fibre Volume ◽  
Coir Fiber ◽  
Linear Regression Models ◽  
Durability Properties ◽  
Coir Fibre ◽  
Mathematical Exploration ◽  
Foamed Concrete

A mathematical exploration using statistical techniques for the prediction of durability properties of foamed concrete with inclusion of coir fibre was performed for the foamed concrete data obtained from laboratory experimental work done in this research. The variable used in the prediction models was the fibre volume fractions. The multiple non-linear regression models yielded exceptional correlation coefficients for the prediction of water absorption, porosity, ultrasonic pulse velocity and depth of carbonation. The mathematical statistical procedures (regression models) that are proposed in this study provide tools of considerable value in the evaluation of durability properties of foamed concrete. The information derived from this procedure is valuable in filtering and refining design criteria and provisions related to foamed concrete with addition of coir fiber.

scholarly journals Does healthcare inequity reflect variations in peoples’ abilities to access healthcare? Results from a multi-jurisdictional interventional study in two high-income countries

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Jeannie Haggerty ◽  
Jean-Frederic Levesque ◽  
Mark Harris ◽  
Catherine Scott ◽  
Simone Dahrouge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Primary Healthcare ◽  
Social Vulnerability ◽  
Regression Models ◽  
Social Systems ◽  
Social Characteristics ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
Wide Range ◽  
Social Vulnerabilities

Abstract Background Primary healthcare services must respond to the healthcare-seeking needs of persons with a wide range of personal and social characteristics. In this study, examined whether socially vulnerable persons exhibit lower abilities to access healthcare. First, we examined how personal and social characteristics are associated with the abilities to access healthcare described in the patient-centered accessibility framework and with the likelihood of reporting problematic access. We then examined whether higher abilities to access healthcare are protective against problematic access. Finally, we explored whether social vulnerabilities predict problematic access after accounting for abilities to access healthcare. Methods This is an exploratory analysis of pooled data collected in the Innovative Models Promoting Access-To-Care Transformation (IMPACT) study, a Canadian-Australian research program that aimed to improve access to primary healthcare for vulnerable populations. This specific analysis is based on 284 participants in four study regions who completed a baseline access survey. Hierarchical linear regression models were used to explore the effects of personal or social characteristics on the abilities to access care; logistic regression models, to determine the increased or decreased likelihood of problematic access. Results The likelihood of problematic access varies by personal and social characteristics. Those reporting at least two social vulnerabilities are more likely to experience all indicators of problematic access except hospitalizations. Perceived financial status and accumulated vulnerabilities were also associated with lower abilities to access care. Higher scores on abilities to access healthcare are protective against most indicators of problematic access except hospitalizations. Logistic regression models showed that ability to access is more predictive of problematic access than social vulnerability. Conclusions We showed that those at higher risk of social vulnerability are more likely to report problematic access and also have low scores on ability to seek, reach, pay, and engage with healthcare. Equity-oriented healthcare interventions should pay particular attention to enhancing people’s abilities to access care in addition to modifying organizational processes and structures that reinforce social systems of discrimination or exclusion.

Mild parkinsonian signs, energy decline, and striatal volume in community-dwelling older adults

2021 ◽  
Author(s):  
Qu Tian ◽  
Rebecca Ehrenkranz ◽  
Andrea L Rosso ◽  
Nancy W Glynn ◽  
Lana M Chahine ◽  
...  
Keyword(s):  
Older Adults ◽  
Regression Models ◽  
Rating Scale ◽  
Gray Matter Volume ◽  
Energy Levels ◽  
Community Dwelling ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
Body Composition Study ◽  
Using Data

Abstract Background Mild Parkinsonian Signs (MPS), highly prevalent in older adults, predict disability. It is unknown whether energy decline, a predictor of mobility disability, is also associated with MPS. We hypothesized that those with MPS had greater decline in self-reported energy levels (SEL) than those without MPS, and that SEL decline and MPS share neural substrates. Methods Using data from the Health, Aging and Body Composition Study, we analyzed 293 Parkinson’s Disease-free participants (83±3 years old, 39% Black, 58% women) with neuroimaging data, MPS evaluation by Unified Parkinson Disease Rating Scale in 2006-2008, and ≥ 3 measures of SEL since 1999-2000. Individual SEL slopes were computed via linear mixed models. Associations of SEL slopes with MPS were tested using logistic regression models. Association of SEL slope with volume of striatum, sensorimotor, and cognitive regions were examined using linear regression models adjusted for normalized total gray matter volume. Models were adjusted for baseline SEL, mobility, demographics, and comorbidities. Results Compared to those without MPS (n=165), those with MPS (n=128) had 37% greater SEL decline in the prior eight years (p=0.001). Greater SEL decline was associated with smaller right striatal volume (adjusted standardized β=0.126, p=0.029). SEL decline was not associated with volumes in other regions. The association of SEL decline with MPS remained similar after adjustment for right striatal volume (adjusted OR=2.03, 95% CI: 1.16 - 3.54). Conclusion SEL decline may be faster in those with MPS. Striatal atrophy may be important for declining energy but does not explain the association with MPS.

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