Acute pancreatitis:A review of diagnosis,severity prediction and prognosis assessment from imaging technology,scoring system and artificial intelligence

Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.

Spatial distribution prediction and benefits assessment of green manure in the Pinggu District,Beijing,based on the CLUE-S model

Green manure use in China has declined rapidly since the 1980 s with the extensive use of chemical fertilizers.The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure.Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting.However,little information is available regarding on where,at what rates,and in which ways（i.e.,intercropping green manure in orchards or rotating green manure in cropland） to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale.This paper presents the conversion of land use and its effects at small region extent（CLUE-S） model,which is specifically developed for the simulation of land use changes originally,to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing,China.Four types of land use for planting or not planting green manure were classified and the future land use dynamics（mainly croplands and orchards） were considered in the prediction.Two scenarios were used to predict the spatial distribution of green manure based on data from 2011：The promotion of green manure planting in orchards（scenario 1） and the promotion of simultaneous green manure planting in orchards and croplands（scenario 2）.The predictions were generally accurate based on the receiver operating characteristic（ROC） and Kappa indices,which validated the effectiveness of the CLUE-S model in the prediction.In addition,the spatial distribution of the green manure was acquired,which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan,the western and southern regions of Wangxinzhuang,the middle region of Shandongzhuang,the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1.Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang,and croplands in most regions of Daxingzhuang,southern Pinggu,northern Xiagezhuang and most of Mafang.The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators.The economic and ecological gains of scenarios 1 and 2 were 175691 900 and143000 300 CNY,respectively,which indicated that the first scenario was more beneficial for promoting the same area of green manure.These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

QoE Assessment of Group Synchronization Control Scheme with Prediction in Work Using Haptic Media

This paper proposes a group synchronization control scheme with prediction in work using haptic media. The scheme adjusts the output timing among multiple terminals and keeps the interactivity high. It outputs position information by predicting the future position later than the position included in the last-received information by a fixed amount of time. It also advances the output time of position information at each local terminal by the same amount of time. We deal with two different types of work using haptic media so as to demonstrate the effectiveness of the scheme. We assess the output quality of haptic media for the two types of work subjectively and objectively by Quality of Experience (QoE) assessment. We further clarify the relationship between subjective and objective assessment results.

Spatial data mining system for ore-forming prediction

The authors designed the spatial data mining system for ore-forming prediction based on the theory and methods of data mining as well as the technique of spatial database,in combination with the characteristics of geological information data.The system consists of data management,data mining and knowledge discovery,knowledge representation.It can syncretize multi-source geosciences data effectively,such as geology,geochemistry,geophysics,RS.The system digitized geological information data as data layer files which consist of the two numerical values,to store these files in the system database.According to the combination of the characters of geological information,metallogenic prognosis was realized,as an example from some area in Heilongjiang Province.The prospect area of hydrothermal copper deposit was determined.

A Prediction Model of MF Radiation in Environmental Assessment

Objective To predict the impact of MF radiation on human health. Methods The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. Results A kind of analogism on the basis of geometric proportion decay pattern is put forward in the essay. It showed that with increasing of height the field intensity increased according to geometric proportion law. Conclusion This geometric proportion prediction model can be used to estimate the impact of MF radiation on inhabited environment, and can act as a reference pattern in predicting the environmental impact level of MF radiation.

Three-dimensional Modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

Neoadjuvant treatment for advanced esophageal cancer: response assessment before surgery and how to predict response to chemoradiation before starting treatment

Patients with advanced esophageal cancer（T3-4, N） have a poor prognosis. Chemoradiation or chemotherapy before esophagectomy with adequate lymphadenectomy is the standard treatment for patients with resectable advanced esophageal carcinoma. However, only patients with major histopathologic response（regression to less than 10% of the primary tumor） after preoperative treatment will have a prognostic benefit of preoperative chemoradiation. Using current therapy regimens about 40% to 50% of the patients show major histopathological response. The remaining cohort does not benefit from this neoadjuvant approach but might benefit from earlier surgical resection. Therefore, it is an aim to develop tools for response prediction before starting the treatment and for early response assessment identifying responders. The current review discusses the different imaging techniques and the most recent studies about molecular markers for early response prediction. The results show that [^18F]-fluorodeoxyglucose-positron emission tomography（FDGPET） has a good sensitivity but the specificity is not robust enough for routine clinical use. Newer positron emission tomography detector technology, the combination of FDG-PET with computed tomography, additional evaluation criteria and standardization of evaluation may improve the predictive value. There exist a great number of retrospective studies using molecular markers for prediction of response. Until now the clinical use is missing. But the results of first prospective studies are promising. A future perspective may be the combination of imaging technics and special molecular markers for individualized therapy. Another aspect is the response assessment after finishing neoadjuvant treatment protocol. The different clinical methods are discussed. The results show that until now no non-invasive method is valid enough to assess complete histopathological response.

Predictive value of the unplanned extubation risk assessment scale in hospitalized patients with tubes

BACKGROUND Critical patients often had various types of tubes,unplanned extubation of any kind of tube may cause serious injury to the patient,but previous reports mainly focused on endotracheal intubation.The limitations or incorrect use of the unplanned extubation risk assessment tool may lead to improper identification of patients at a high risk of unplanned extubation and cause delay or nonimplementation of unplanned extubation prevention interventions.To effectively identify and manage the risk of unplanned extubation,a comprehensive and universal unplanned extubation risk assessment tool is needed.AIM To assess the predictive value of the Huaxi Unplanned Extubation Risk Assessment Scale in inpatients.METHODS This was a retrospective validation study.In this study,medical records were extracted between October 2020 and September 2021 from a tertiary comprehensive hospital in southwest China.For patients with tubes during hospitalization,the following information was extracted from the hospital information system:age,sex,admission mode,education,marital status,number of tubes,discharge mode,unplanned extubation occurrence,and the Huaxi Unplanned Extubation Risk Assessment Scale(HUERAS)score.Only inpatients were included,and those with indwelling needles were excluded.The best cut-off value and the area under the curve(AUC)of the Huaxi Unplanned Extubation Risk Assessment Scale were been identified.RESULTS A total of 76033 inpatients with indwelling tubes were included in this study,and 26 unplanned extubations occurred.The patients’HUERAS scores were between 11 and 30,with an average score of 17.25±3.73.The scores of patients with or without unplanned extubation were 22.85±3.28 and 17.25±3.73,respectively(P<0.001).The results of the correlation analysis showed that the correlation coefficients between each characteristic and the total score ranged from 0.183 to 0.843.The best cut-off value was 21,and there were 14135 patients with a high risk of unplanned extubation,accounting for 18.59%.The Cronbach’sα,sensitivity,specificity,positive predictive value,and negative predictive value of the Huaxi Unplanned Extubation Risk Assessment Scale were 0.815,84.62%,81.43%,0.16%,and 99.99%,respectively.The AUC of HUERAS was 0.851(95%CI:0.783-0.919,P<0.001).CONCLUSION The HUERAS has good reliability and predictive validity.It can effectively identify inpatients at a high risk of unplanned extubation and help clinical nurses carry out risk screening and management.

Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China

Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.

Establishment of Prediction Method of Tourism Meteorological Index in Langzhong Ancient City

Based on the meteorological data of Langzhong from 2010 to 2020,the human body comfort index was calculated,and tourism climate comfort was evaluated to establish the prediction equation of tourism meteorological index.OLS was used to compare the correlation between actual tourist flow and tourism meteorological index and test the model effect.Average correlation coefficient R was 0.7017,so the correlation was strong,and P value was 0.The two were significantly correlated at 0.01 level(bilateral).It can be seen that the forecast equation of tourism meteorological index had a strong correlation with the actual number of tourists,and the predicted value was basically close to the actual situation,and the forecast effect is good.

Evaluation of the Climate Prediction System CMA-CPSv3 on Performances of Precipitation and Temperature Prediction over the Tibetan Plateau

In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration (CMA-CPSv3) over the Tibetan Plateau region, the precipitation and temperature predicted by CMA-CPSv3 at different lead time was evaluated for the period of 2001-2023, by comparing with observations—the monthly precipitation of the CMAP and 2 m temperature data of the NCEP/DOE reanalysis data. The main conclusions were as follows: 1) the forecast skill of the model is very sensitive to the initial conditions and value, and the model forecast capability decreases rapidly as the lead time is extended. 2) CPSv3 performed well in capturing the spatial distribution of precipitation over Tibet in January, August, October, November and December at 0-month lead, and the forecast skill is relatively poor in March and April. CPSv3 has better performance in the east-central part of the land in January, in the central and western part of the region in March, in the whole region in April, in the northern part of Nagchu, northern part of Chamdo and central part of Shigatse in July, in the eastern part of Chamdo, northern part of Nagchu and northern part of Ali in August, in the whole region in September, and in the cities of Shigatse and Shannan in November. 3) At each lead time, the forecast skill for 2-m temperatures was higher than that for precipitation. For 2-m temperature, CPSv3 performed best in January, May, July, August, September, October, and December, while performing relatively poor in March, April, and November. Specifically, the prediction skill is higher in January and December for most of the regions, for Shigatse and Ali in May, for southern Shannan in July, and for northern Nagchu and southern Shannan in August, and there is some prediction skill in March, April and October, while CPSv3 performed poorly in November.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Innovative approaches in predicting outcomes for rectal neuroendocrine tumors

Rectal neuroendocrine neoplasms pose significant challenges due to their varied presentations and prognoses.Traditional prognostic models,while useful,often fall short of accurately predicting clinical outcomes for these patients.This article discusses the development and implications of a novel prognostic tool,the GATIS score,which aims to enhance predictive accuracy and guide treatment strategies more effectively than current methods.Utilizing data from a large cohort and employing sophisticated statistical models,the GATIS score integrates clinical and pathological markers to provide a nuanced assessment of prognosis.We evaluate the potential of this score to transform clinical decision-making processes,its integration into current medical practices,and future directions for its develo-pment.The integration of genetic markers and other biomarkers could further refine its predictive power,highlighting the ongoing need for innovation in the management of rectal neuroendocrine neoplasms.

Fuzzy comprehensive assessment of running condition for a large-scale centrifugal compressor set

A fuzzy comprehensive assessment method of running condition was constructed and applied to a large-scale centrifugal compressor set in a petrochemical corporation aiming at the monitoring and early warning of abnormal conditions in industry.The maximal information coefficient(MIC)correlation analysis of indexes was introduced to determine the independent indexes to be assessed,and the dynamic deterioration degree was calculated using the predicted independent indexes by the second-order Markov chain model.The fuzzy membership degree weighting method was employed to assess the running condition of all units in the set.Simple and direct radar chart was used to visualize condition assessment grades.Results showed that the proposed fuzzy comprehensive assessment method successfully assessed the running condition of the set.The constructed method achieved 10 min earlier alarm than the traditional threshold alarm occurred at the specific moment of00:44 on April 7 of 2018.The method would provide a valuable tool and have a wide engineering application in ensuring the safety and reliability of industrial production.

Drug-induced liver injury:Towards early prediction and risk stratification

Drug-induced liver injury(DILI) is a hot topic for clinicians, academia, drug companies and regulators, as shown by the steadily increasing number of publications and agents listed as causing liver damage(http://livertox.nih.gov/). As it was the case in the past decade with drug-induced QT prolongation/arrhythmia, there is an urgent unmet clinical need to develop tools for risk assessment and stratification in clinical practice and, in parallel, to improve prediction of pre-clinical models to support regulatory steps and facilitate early detection of liver-specific adverse drug events. Although drug discontinuation and therapy reconciliation still remain the mainstay in patient management to minimize occurrence of DILI, especially acute liver failure events, different multidisciplinary attempts have been proposed in 2016 to predict and assess drug-related risk in individual patients; these promising, albeit preliminary, results strongly support the need to pursue this innovative pathway.

A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

In this article,an approach for economic performance assessment of model predictive control(MPC) system is presented.The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in variance.The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction.Combining the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization problem.The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.

Flight Safety System Evaluation and Optimal Linear Prediction

The complexity of flight safety system is usually affected by a variety of uncertainties.The uncertainty of overall security situation of flight safety system are hardly determined.In this work,flight safety assessment index system is firstly established based on software hardware environment liveware management(SHELM)model.And flight safety assessment is also carried out with matter-element theory algorithm to obtain safety state.According to correlation degree values of each evaluation index,key indexes affected flight safety are obtained.Under the assumption that the flight safety system is a linear dynamic system and combining the above evaluation analysis,Kalman filter algorithm is used to carry out prediction analysis on security situation.A simulation analysis is carried out based on an actual flight safety situation of an airline.The results show that the security state of airline flight safety system in a short period of time can be obtained,and main factors affecting flight safety are found out.This provides a viable way for airlines to further strengthen flight safety management.

Probabilistic seismic hazard assessment of Kazakhstan and Almaty city in peak ground accelerations

As for many post-soviet countries, Kazakhstan’s building code for seismic design was based on a deterministic approach. Recently, Kazakhstan seismologists are engaged to adapt the PSHA(probabilistic hazard assessment) procedure to the large amount of available geological, geophysical and tectonic Kazakh data and to meet standard requirements for the Eurocode 8. The new procedure has been used within National projects to develop the Probabilistic GSZ(General Seismic Zoning) maps of the Kazakhstan territory and the SMZ(Probabilistic Seismic Microzoning) maps of Almaty city. They agree with the seismic design principles of Eurocode 8 and are expressed in terms of not only seismic intensity,but also engineering parameters(peak ground acceleration PGA). The whole packet of maps has been developed by the Institute of Seismology, together with other Kazakhstan Institutions. Our group was responsible for making analysis in PGA. The GSZ maps and hazard assessment maps for SMZ in terms of PGA for return periods 475 and 2475 years are considered in the article.