Prediction Of Deterioration Research Articles

Development of Continual Deterioration Prediction Models for Cement Stabilized Base

Development of Continual Deterioration Prediction Models for Cement Stabilized Base

Improved similarity assessment and spectral clustering for unsupervised linking of data extracted from bridge inspection reports

Textual bridge inspection reports are important data sources for supporting data-driven bridge deterioration prediction and maintenance decision making. Information extraction methods are available to extract data/information from these reports to support data-driven analytics. However, directly using the extracted data/information in data analytics is still challenging because, even within the same report, there exist multiple data records that describe the same entity, which increases the dimensionality of the data and adversely affects the performance of the analytics. The first step to address this problem is to link the multiple records that describe the same entity and same type of instances (e.g., all cracks on a specific bridge deck), so that they can be subsequently fused into a single unified representation for dimensionality reduction without information loss. To address this need, this paper proposes a spectral clustering-based method for unsupervised data linking. The method includes: (1) a concept similarity assessment method, which allows for assessing concept similarity even when corpus or semantic information is not available for the application at hand; (2) a record similarity assessment method, which captures and uses similarity assessment dependencies to reduce the number of falsely-linked records; and (3) an improved spectral clustering method, which uses iterative bi-partitioning to better link records in an unsupervised way and to address the transitive closure problem. The proposed data linking method was evaluated in linking records extracted from ten bridge inspection reports. It achieved an average precision, recall, and F-1 measure of 96.2%, 88.3%, and 92.1%, respectively.

Aspects of abrupt deterioration prediction by general nurses: including the personal characteristics related to prediction

Aspects of abrupt deterioration prediction by general nurses: including the personal characteristics related to prediction

Reinforced concrete long-term deterioration prediction for the implementation of a Bridge Management System

Nowadays, an increasing number of reinforced concrete (RC) bridges require refurbishment and repair works due to their advanced deterioration. However, the level of damage that RC structures may experience due to the action of different aggressive agents is not always evident to the naked eye, representing risks for bridges’ owners, concessionaires, and users.Whereas the control of the mechanical deterioration and damage of bridges has reached a high level of development in recent years, the monitoring of the deterioration due to physical, chemical, or biological processes is currently in a less developed stage. For instance, corrosion and carbonation evolution rates of existing structures depend on different threshold values that may differ significantly from the standard values proposed for design purposes of new constructions. Moreover, these threshold values depend on several parameters such as temperature, type of cement, or environmental conditions (e.g., dry-wet cycles). Therefore, durability prediction models must also include an assessment of the evolution of the structural performance exposed to the aggressive actions of various agents simultaneously.During the last decades, many administrations have been implementing Bridge Management Systems (BMS) to control their infrastructures through periodic inspections to detect damages and reduce the risk as well as the uncertainty about the level of investment required to repair a certain structure. The collection of these data results in inspection databases which include large amounts of information that can be used to understand how RC structures deteriorate under different environmental aggressiveness conditions.This paper focuses on the definition of criteria for the implementation of an inspections-based BMS for RC bridges. For the purpose of this paper, since extensive inspections databases are needed to perform the analyses described, real data from the ‘AURA’ BMS have been used, which includes a database of hundreds of Spanish RC bridges’ inspections.

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

Data-driven prediction of long-term deterioration of RC bridges

During the last decades, an increasing number of administrations have been implementing Bridge Management Systems (BMS) to control their infrastructure and detect damages that may require reparation or further analysis. The collection of these data results in inspection databases which include large amounts of information that can be used to understand how reinforced concrete (RC) structures deteriorate over time under different environmental aggressiveness conditions. Based on collected information of 298 roadway RC bridges, this paper demonstrates how the statistical analysis of the data obtained from inspections can be used to predict their ageing evolution over time in a reliable way.

Lifestyle Variables Such as Daily Internet Use, as Promising Protective Factors against Cognitive Impairment in Patients with Subjective Memory Complaints. Preliminary Results.

Subjective memory complaints (SMCs) may be important markers in the prediction of cognitive deterioration. The aim of this study was to find associations between individual lifestyle factors, which may contribute to cognitive impairment (CI) in people with SMCs and to conduct a literature review on the relationship between internet use and CI in subjects over 50 years old, as a related factor. This was a case-controlled study that included 497 subjects aged over 50 years with SMCs who were recruited from 19 community pharmacies. Three screening tests were used to detect possible CIs, and individuals with at least one test result compatible with a CI were referred to primary care for evaluation. Having self-referred SMC increased the odds of obtaining scores compatible with CI and this factor was significantly related to having feelings of depression (OR = 2.24, 95% CI [1.34, 3.90]), taking anxiolytics or antidepressants (OR = 1.93, 95% CI [1.23, 3.05]), and being female (OR = 1.83, 95% CI [1.15, 2.88]). Thirty percent of our sample obtained scores compatible with CI. Age over 70 years increased the odds of obtaining scores compatible with CI. A high-level education, reading, and daily internet use were factors associated with a reduced risk of positive scores compatible with CI (37–91%, 7–18%, and 67–86%, respectively), while one extra hour television per day increased the risk by 8–30%. Among others, modifiable lifestyle factors such as reading, and daily internet usage may slow down cognitive decline in patients over 50 with SMCs. Four longitudinal studies and one quasi-experimental study found internet use to be beneficial against CI in patients over 50 years of age.

Biomarkers of heart failure: current state of problem

There is constant increase in patients with heart failure every year worldwide. Early diagnosis and prediction of deterioration could upgrade management of patients and slow down the progression of heart failure.The brain natriuretic peptide precursor (NT-proBNP) is considered to be the universal biomarker, although it has several limitations. The search of ideal biomarker is directed into molecular biology and genetics. Microribonucleic acids (microRNAs) regulate different processes in human body, present myocardial specificity, and plasma stability. It has been proven in different trials that diagnostic and prognostic level of microRNAs is equal to NT-proBNP. Potential opportunities of the method are not only diagnosis but therapeutic targets for heart failure

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized
 Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained.
 Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting.
 Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

Feature Selection and Deep Learning for Deterioration Prediction of the Bridges

Bridge deterioration is inevitable in service, and the inspection and maintenance of bridges are needed to ensure structural integrity. To make cost-effective inspection plans, bridge management departments need degradation models to predict future condition ratings of bridges. Although there have been studies on bridge degradation, the input features of models are selected mostly based on engineering experience, and no effective method has been proposed. Meanwhile, most models based on machine learning (ML) and deep learning (DL) only predict the degradation of bridges in a single year and cannot cover a complete inspection cycle (usually 2 years), providing limited decision support for the transportation departments. Besides, more accurate models are needed to predict the degradation trend of bridges. In response to these problems, an improved ReliefF algorithm is proposed to select features of bridges in the paper. Meanwhile, a new degradation model combining recurrent neural network (RNN) and convolutional neural network (CNN) is established. Historical data of bridges in the US state of Texas from 1992 to 2019 are employed to verify the proposed methods. The result shows that the improved ReliefF algorithm selects the appropriate feature set as the input of the prediction model, and the model accurately predicts the future condition ratings of bridges in the next 3–4 years. The research is beneficial to infrastructure management departments in allocating bridge inspection and maintenance resources reasonably.

Utility of Dual Monitoring of the Lower Cranial Nerve Motor-Evoked Potentials Threshold Level Criterion to Predict Swallowing Function in Skull Base and Brainstem Surgery.

Evaluate the value of bilateral final/baseline threshold level changes of lower cranial nerve MEPs in postoperative swallowing function deterioration prediction. Bilateral lower cranial nerve motor-evoked potentials (MEPs) were recorded in 51 patients who underwent treatment for skull base and brainstem tumors. Corkscrew-like electrodes were positione 2 cm below C3/C4 and Cz. The MEPs were recorded from different muscle groups, including the posterior pharyngeal wall muscle, tongue muscle, genioglossus muscle, and cricothyroid muscle through paired needle electrodes. Swallowing function was assessed clinically using the Mann Assessment of Swallowing Ability score before and after the procedure at 7 days, 1 month, and 3 months. Bilateral final/baseline threshold level increases in lower cranial nerve MEPs under the dual monitoring were significantly correlated with postoperative swallowing function deterioration ( r = 0.660 at 7 days, r = 0.735 at 1 month, and r = 0.717 at 3 months; p < 0.05). Bilateral final/baseline threshold level changes of more than 20% were recorded in 23 of the 51 patients, with 21 patients experiencing swallowing function deterioration postoperatively. The other 28 patients had bilateral threshold level changes of less than 20%, with 26 patients maintaining or improving their swallowing function, and 12 of those patients presented transient deterioration of swallowing function in the early postoperative period. Dual monitoring of lower cranial nerves and their different muscle groups MEPs was a safe and effective way to predict postoperative swallowing function. An increase in bilateral final/baseline threshold level change of more than 20% was predictive of permanent swallowing deterioration, especially in patients with poor swallowing function preoperatively.

Maternal plasma serotonin level not suitable as postpartum depression diagnostic biomarker: Results from a prospective cohort study

BackgroundWhether plasma serotonin (5-HT) levels could be a biomarker for postpartum depression (PPD) diagnosis is under dispute. MethodsA total of 979 of pregnant women without antenatal depression at the time of delivery (TD) were enrolled and followed up at six weeks postpartum (SWP) in Changsha, China. The odds ratio (OR) and 95% confidence interval (CI) for plasma 5-HT level at TD, at SWP, changes in 5-HT, and risk of PPD and deterioration in EPDS scores at SWP were estimated by Logistic regressions. Restricted cubic spline (RCS) functions were also used to assess the dose-response relationships. ResultsThe 6-week cumulative incidence of PPD was 12.05% (95%CI:10.08%, 14.26%). The average level of plasma 5-HT changed from 223.65 ± 131.47 ng/ml at TD to 216.43 ± 122.73 ng/ml at SWP, with an average change of −7.22 ± 96.54 ng/ml. Plasma 5-HT at TD was negatively correlated with EPDS score at TD and SWP (p < 0.05), as was the correlation between 5-HT at SWP and EPDS scores at SWP (p = 0.038). However, the changes in 5-HT were not associated with the EPDS score at SWP (p = 0.346). Neither plasma 5-HT level at TD nor changes in 5-HT was associated with PPD at SWP or deterioration in EPDS scores (p < 0.05). Plasma 5-HT at delivery had insignificant discriminatory power for diagnosing PPD and prediction of deterioration in EPDS scores (p ≥ 0.05). ConclusionPlasma 5-HT level at delivery was associated with EPDS score at delivery and SWP, but not with PPD at SWP suggesting that plasma 5-HT is not suitable as PPD diagnostic biomarker.

Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.

Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

We aimed to develop a novel prediction model for early neurological deterioration(END) based on an interpretable machine learning(ML) algorithm for atrial fibrillation(AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models.

Predictors of deterioration of mild cases with COVID-19 during the third wave

Background: The global pandemic of corona virus disease 2019 (COVID-19) still growing and its death toll has been progressing massively as well disregarding the human and financial resources. This necessitates early detection of severe cases and treats them promptly. Thus, in this study, we aimed at identification of predictors of deterioration of mild cases of COVID-19. Methods: In this case control study, we included 137 subjects with confirmed COVID-19 from Suez Canal University Hospital. Baseline characteristics, laboratory and radiological data were documented. These variables were compared between those who witnessed deterioration (cases) and those who did not (control). Logistic regression analysis was performed to identify predictors of deterioration. Results: Fifteen out of 137 patients experienced clinical deterioration. Age, marital status, smoking status, diabetes mellitus (DM), hypertension, chronic pulmonary disease and chronic liver disease (CLD), and high mean of computed topography (CT score) were more prevalent among deteriorated patients; nevertheless, anosmia and loss of taste were characteristic of non-deteriorated cases. The latter group had relatively low mean lymphocytes, neutrophils/lymphocytes ratio (N/L ratio), albumin, and high mean of inflammatory markers. Logistic regression revealed that only diabetes, chronic liver disease, albumin, LDH, CT score were significant predictors of deterioration.Analysis of the receiver operating characteristic curve revealed that CT score was the most sensitive and specific indicator for prediction of deterioration. Conclusions: Many clinical data, laboratory and radiological features were more prominent in deteriorated cases, but DM, CLD, albumin, LDH and CT score were significant predictors of deterioration.

Analysis and prediction of heat transfer deterioration of supercritical pressure cryogenic methane in a vertical tube

Developing a compact vaporizer with high thermal efficiency is of great significance to the storage and usage of Liquid Natural Gas (LNG). Numerical simulations were performed to investigate the flow and heat transfer characteristics of supercritical pressure methane in a vertical tube, and primary focus was to reveal the mechanism of two-peak wall temperatures occurrence. Six turbulent models have been confronted with experimental data, and the Renormalization Group (RNG) k-ε model is adopted. When Heat Transfer Deterioration (HTD) occurs, two-peak wall temperatures appear in the upward flow with high mass flux. The phenomenon of two-peak wall temperatures were explained in detail based on radial distributions of velocity and turbulent kinetic energy at different axial positions. The “M”-shape velocity distribution promotes the generation of turbulence in the core region. The reversal of radial flow direction enhances the mixing between hot and cold fluids. A new criterion was proposed for predicting the critical heat flux which causes the onset of HTD in supercritical pressure methane.

Is There a Place for Induction Therapy With Polyclonal Antibodies to Improve Renal Function After Liver Transplantation?

Is There a Place for Induction Therapy With Polyclonal Antibodies to Improve Renal Function After Liver Transplantation?

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

PurposeWhile more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.MethodsWe developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).ResultsThe LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.ConclusionWe present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data

ObjectivesEarly recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.MethodsAn artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.ResultsA multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).ConclusionsUsing CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.Key Point • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-08049-8.

Deterioration prediction of existing concrete bridges using a LSTM recurrent neural network

Bridge censored databases can be used to analyze and assess structural deterioration conditions, but conducting the analysis is difficult. This difficulty occurs because many factors affect deterioration, and the time span of the data for these factors depends on the years in service of the respective bridge. In addition, the values of some factors are not regularly observed. The present study uses the long short-term memory (LSTM) to consider twelve potentially influencing factors to recognize the relationships between these factors and deterioration grades. Testing the model on an inspection database of 3,368 bridges indicates that the LSTM model obtained an accuracy of exceeding 80%, i.e., outperforms the performance of a multilayer perceptron model established using the same database. For four types of bridges, the LSTM model shows equivalent performance. In addition, the predictive ability of the LSTM model for coastal bridges is slightly superior to non-coastal bridges. No significant differences in accuracy are determined between different deck areas. Practically, the model can predict bridge deterioration paths, and could help decision-makers formulate predictive intervention strategies for improving the quality of maintenance management.