Application of time series prediction techniques for coastal bridge engineering

ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In...

Influenza is a highly contagious respiratory disease and is still a serious threat to public health all over the world. Forecasting techniques help in monitoring seasonal influenza and other influenza-like diseases and also in managing resources appropriately to formulate vaccination strategies and choose appropriate public health measures to reduce the impact of the disease. The aim of this investigation is to forecast the monthly incidence of seasonal flu in Saudi Arabia for the years 2020 and 2021 using the XGBoost model and compare it with ARIMA and SARIMA models. The results show that the XGBoost model has the lowest values MAE, MAE, and RMSE compared to the ARIMA and SARIMA models and the highest value of R-squared (R²). This study compares the accuracy of the XGBoost model with ARIMA and SARIMA models in providing a forecast of the number of monthly seasonal influenza cases. These results confirm the notion that the XGBoost model has a higher accuracy of prediction than that of the ARIMA and SARIMA models, mainly due to its capacity to capture complex nonlinear relationships. Therefore, the XGBoost model could predict monthly occurrences of seasonal influenza cases in Saudi Arabia.

ObjectivesA radiomics-based explainable eXtreme Gradient Boosting (XGBoost) model was developed to predict central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC), including positive and negative effects.MethodsA total of 587 PTC patients admitted at Binzhou Medical University Hospital from 2017 to 2021 were analyzed retrospectively. The patients were randomized into the training and test cohorts with an 8:2 ratio. Radiomics features were extracted from ultrasound images of the primary PTC lesions. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select CCLNM positively-related features and radiomics scores were constructed. Clinical features, ultrasound features, and radiomics score were screened out by the Boruta algorithm, and the XGBoost model was constructed from these characteristics. SHapley Additive exPlanations (SHAP) was used for individualized and visualized interpretation. SHAP addressed the cognitive opacity of machine learning models.ResultsEleven radiomics features were used to calculate the radiomics score. Five critical elements were used to build the XGBoost model: capsular invasion, radiomics score, diameter, age, and calcification. The area under the curve was 91.53% and 90.88% in the training and test cohorts, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, radiomics score, diameter, and calcification) and negative (i.e., age) impacts. The XGBoost model outperformed the radiologist, increasing the AUC by 44%.ConclusionsThe radiomics-based XGBoost model predicted CCLNM in PTC patients. Visual interpretation using SHAP made the model an effective tool for preoperative guidance of clinical procedures, including positive and negative impacts.

Background This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). Methods This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. Results There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. Conclusions In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Compared with coronary artery bypass grafting (CABG) under cardiopulmonary bypass, off-pump coronary artery bypass (OPCAB) is minimally invasive and reduces the risk of intraoperative blood transfusion and acute kidney injury. Nonetheless, OPCAB-related complications still pose a threat. Machine learning technology can analyze a large number of clinical data, establish risk prediction models and help clinicians make early and correct clinical decisions. Risk prediction models are available for mortality and morbidity after cardiac surgery, but they are not specific to OPCAB. This study aimed to develop a predictive model of severe complications after OPCAB, based on machine learning. Anesthesia records of OPCAB from the General Hospital of the Northern Theater Command (Shenyang, China) collected between January 1, 2019, and June 15, 2020, were analyzed. The endpoint of the study was the occurrence of serious complications after OPCAB (postoperative unplanned intra-aortic balloon pump, secondary surgery and death). The features entered into the models were as follows: intraoperative ventricular fibrillation, number of saphenous vein grafts, nerve block (NeB), venous oxygen saturation (SvO2), skin incision-bypass time, and hypertension. A total of 8 machine learning algorithms were tested: logistic regression analysis (LRA), k-nearest neighbor (KNN), naïve Bayes (NB), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical features gradient boosting (CatBoost). Among the 506 patients found in the records, 27 met the endpoint. The highest area under the curve (AUC) value was achieved with the XGBoost model (AUC = 0.94), and the lowest with the SVM model (AUC = 0.75). The highest and lowest accuracy were observed with the XGBoost and NB models, respectively, while the highest and lowest precision were achieved using the SVM and NB models, respectively. Based on the receiver operating characteristic (ROC) curves, the XGBoost model was selected as the most useful in this study. This study suggests using the XGBoost model to predict the risk of complications after OPCAB.

The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment. To investigate whether machine learning models trained on routine clinical data from electronic health records (EHRs) can predict diagnostic progression to schizophrenia or bipolar disorder among patients undergoing treatment in psychiatric services for other mental illness. This cohort study was based on data from EHRs from the Psychiatric Services of the Central Denmark Region. All patients aged 15 to 60 years with at least 2 contacts (at least 3 months apart) with the Psychiatric Services of the Central Denmark Region between January 1, 2013, and November 21, 2016, were included. Analysis occurred from December 2022 to November 2024. Predictors based on EHR data, including medications, diagnoses, and clinical notes. Diagnostic transition to schizophrenia or bipolar disorder within 5 years, predicted 1 day before outpatient contacts by means of elastic net regularized logistic regression and extreme gradient boosting (XGBoost) models. The area under the receiver operating characteristic curve (AUROC) was used to determine the best performing model. The study included 24 449 patients (median [Q1-Q3] age at time of prediction, 32.2 [24.2-42.5] years; 13 843 female [56.6%]) and 398 922 outpatient contacts. Transition to the first occurrence of either schizophrenia or bipolar disorder was predicted by the XGBoost model, with an AUROC of 0.70 (95% CI, 0.70-0.70) on the training set and 0.64 (95% CI, 0.63-0.65) on the test set, which consisted of 2 held-out hospital sites. At a predicted positive rate of 4%, the XGBoost model had a sensitivity of 9.3%, a specificity of 96.3%, and a positive predictive value (PPV) of 13.0%. Predicting schizophrenia separately yielded better performance (AUROC, 0.80; 95% CI, 0.79-0.81; sensitivity, 19.4%; specificity, 96.3%; PPV, 10.8%) than was the case for bipolar disorder (AUROC, 0.62, 95% CI, 0.61-0.63; sensitivity, 9.9%; specificity, 96.2%; PPV, 8.4%). Clinical notes proved particularly informative for prediction. These findings suggest that it is possible to predict diagnostic transition to schizophrenia and bipolar disorder from routine clinical data extracted from EHRs, with schizophrenia being notably easier to predict than bipolar disorder.

Development and validation of interpretable machine learning model for pre-treatment predicting the response to targeted and immune therapy in hepatocellular carcinoma.

Glass forming ability prediction of bulk metallic glasses based on fused strategy

The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash-Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010–2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the R2 score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon.

ObjectiveTo compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns.MethodsFor this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve.ResultsOf the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns.ConclusionsThe XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.

Predicting corrosion current density in magnesium alloy battery anodes: machine learning approach using rapid miner

Sepsis-related mortality rates are high among elderly patients, especially those in intensive care units (ICUs). Early prediction of the prognosis of sepsis is critical, as prompt and effective treatment can improve prognosis. Researchers have predicted mortality and the development of sepsis using machine learning algorithms; however, few studies specifically focus on elderly patients with sepsis. This paper proposes a viable model for early prediction of in-hospital mortality among elderly patients diagnosed with sepsis. We extracted patient information from the Medical Information Mart for Intensive Care IV database. We employed several machine learning algorithms to predict the in-hospital mortality of elderly ICU patients with sepsis. The performance of the model was evaluated by using the AUROC and F1 score. Furthermore, the SHAP algorithm was used to explain the model, analyze how the individual features affect the model output, and visualize the Shapley value for a single individual. Our study included 18522 elderly patients, with a mortality of 15.4%. After screening, 59 clinical variables were extracted to develop models. Feature importance analysis showed that age, PO2, RDW, SPO2, WBC, and urine output were significantly related to the in-hospital mortality. According to the results of AUROC (0.871 (95% CI: 0.854–0.888)) and F1 score (0.547 (95% CI: 0.539–0.661)) analyses, the extreme gradient boosting (XGBoost) model outperformed the other models (i.e., LGBM, LR, RF, DT, and KNN). Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death. XGBoost machine learning framework gives good in-hospital mortality prediction of elderly patients with sepsis and can maximize prediction model accuracy. The XGBoost model could be an effective tool to assist doctors in identifying high-risk cases of in-hospital mortality among elderly patients with sepsis. This could be used to create a clinical decision support system in the future.

BackgroundPrior research has demonstrated that nutrition plays a crucial role in the establishment and maturation of the reproductive lifetime. Although the specific dietary components involved in preventing or postponing the reproductive lifespan are still unknown, a healthy diet can affect the reproductive lifespan. Here, the study aimed to explore the relationship between reproductive lifespan and diet quality by utilizing the Healthy Eating Index-2015 (HEI-2015).MethodsIn this study, a total of 2761 postmenopausal women were selected from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2016. Diet quality was determined using HEI-2015 based on two 24-hour dietary recalls. Reproductive lifespan was defined as the number of years between self-reported age at menarche and menopause. Weighted linear regression and eXtreme Gradient Boosting (XGBoost) models were used to analyze the relationship between HEI-2015 and reproductive lifespan. Subsequently, the impact of various components of HEI-2015 on reproductive lifespan was assessed through weighted quantile sum (WQS) regression models.ResultsAmong 2761 postmenopausal women, the mean age was 63.7 years. 41.5% were obese, and 49.7% were non-Hispanic white. After adjusting for sociodemographic characteristics, lifestyle factors, and medical history, individuals in the highest tertile of HEI-2015 had a 4.81% (95% CI: 1.82–7.79%) longer reproductive time life. Higher HEI-2015 was also significantly associated with a higher likelihood of late menopause (p for trend < 0.05). Based on XGBoost models, the relative importance of HEI-2015 on reproductive lifespan was determined. Whole fruits, whole grains, total protein foods, and greens and beans significantly contributed to extending age at menopause and reproductive time life in the HEI-2015. The weights of the WQS index for age at menopause were 27.1%, 23.2%, 10.1%, and 7.5% respectively, while the weights of the WQS index for reproductive time life were 30.2%, 14.6%, 9.3%, and 14.0% respectively.ConclusionThere is a positive association between the HEI-2015 and reproductive lifespan. This underscores the significance of enhancing adherence to healthy dietary patterns in preventing a shorter reproductive lifespan.