Assessing recent recurrence after hepatectomy for hepatitis Brelated hepatocellular carcinoma by a predictive model based on sarcopenia

BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Development and validation of a predictive model for patients with post-extubation dysphagia

BACKGROUND:Swallowing disorder is a common clinical symptom that can lead to a series of complications,including aspiration,aspiration pneumonia,and malnutrition.This study aimed to investigate risk factors of post-extubation dysphagia(PED)in intensive care unit(ICU)patients with endotracheal intubation,and to develop a risk-predictive model for PED,which could serve as an assessment tool for the prevention and control of PED.METHODS:Patients retrospectively selected from June to December 2021 in a tertiary hospital served as the derivation cohort.Patients recruited from the same hospital from March to June 2022served as the external validation cohort for the predictive model.We used a combination of variable screening and least absolute shrinkage and selection operator(LASSO)regression to select the most useful candidate predictors and checked the multicollinearity of independent variables using the variance inflation factor method.Multivariate logistic regression analysis was performed to calculate the odds ratio(OR;95%confidence interval[95%CI])and P-value for each variable to predict diagnosis.The screened risk factors were introduced into R software to build a nomogram model.The performance of the model,including discrimination ability,calibration,and clinical benefit,was evaluated by plotting the receiver operating characteristic(ROC),calibration,and decision curves.RESULTS:A total of 305 patients were included in this study.Among them,235 patients(53PED vs.182 non-PED)were enrolled in the derivation cohort,while 70 patients(17 PED vs.53 nonPED)were enrolled in the validation cohort.The independent predictors included age,pause of sedatives,level of consciousness,activities of daily living(ADL)score,nasogastric tube,sore throat,and voice disorder.These predictors were used to establish the predictive nomogram model.The model demonstrated good discriminative ability,and the area under the ROC curve(AUC)was 0.945(95%CI 0.904-0.970).Applying the predictive model to the validation cohort demonstrated good discrimination with an AUC of 0.907(95%CI 0.831-0.983)and good calibration.The decision-curve analysis of this nomogram showed a net benefit of the model.CONCLUSION:A predictive model that incorporates age,pause of sedatives,level of consciousness,ADL score,nasogastric tube,sore throat,and voice disorder may have the potential to predict PED in ICU patients.

Development and validation of a predictive model for the assessment of potassium-lowering treatment among hyperkalemia patients

BACKGROUND:Hyperkalemia is common among patients in emergency department and is associated with mortality.While,there is a lack of good evaluation and prediction methods for the effi cacy of potassium-lowering treatment,making the drug dosage adjustment quite diffi cult.We aimed to develop a predictive model to provide early forecasting of treating eff ects for hyperkalemia patients.METHODS:Around 80%of hyperkalemia patients(n=818)were randomly selected as the training dataset and the remaining 20%(n=196)as the validating dataset.According to the serum potassium(K+)levels after the fi rst round of potassium-lowering treatment,patients were classifi ed into the eff ective and ineff ective groups.Multivariate logistic regression analyses were performed to develop a prediction model.The receiver operating characteristic(ROC)curve and calibration curve analysis were used for model validation.RESULTS:In the training dataset,429 patients had favorable eff ects after treatment(eff ective group),and 389 had poor therapeutic outcomes(ineff ective group).Patients in the ineff ective group had a higher percentage of renal disease(P=0.007),peripheral edema(P<0.001),oliguria(P=0.001),or higher initial serum K+level(P<0.001).The percentage of insulin usage was higher in the effective group than in the ineff ective group(P=0.005).After multivariate logistic regression analysis,we found age,peripheral edema,oliguria,history of kidney transplantation,end-stage renal disease,insulin,and initial serum K+were all independently associated with favorable treatment eff ects.CONCLUSION:The predictive model could provide early forecasting of therapeutic outcomes for hyperkalemia patients after drug treatment,which could help clinicians to identify hyperkalemia patients with high risk and adjust the dosage of medication for potassium-lowering.

A meta-analysis of risk factors for epilepsy after acute ischaemic stroke and the development of a predictive model

Objective:To screen risk factors for epilepsy after acute ischaemic stroke based on meta-analysis and cohort study and to establish a predictive model.Methods:Computer searches of MEDLINE,Embase,Cochrane library,Web of Scinence,PubMed,CNKI,and WanFang Data data were conducted to collect literature on epilepsy after in acute ischemic stroke,from database creation to September 1,2022.The RRs and their 95%confidence intervals(CI)for risk factors for post stroke epilepsy were extracted for each study,and pooled estimates of the RRs and 95%CIs for each study were generated using either a random-effects model or a fixed-effects model.Beta coefficients for each risk factor were calculated based on the combined RR and their corresponding 95%CIs.The beta coefficients were multiplied by 10 and rounded.Results:Ten articles were identified for final inclusion in this meta-analysis,with a total of 141948 cases and 3702 cases of post stroke epilepsy.The risk factors included in the final risk prediction model were infarct size(RR 4.67,95%CI 1.41~15.47;P=0.01),stroke recuRRence(RR 2.48,95%CI 2.01~3.05;P<0.00001),stroke etiology(RR 1.70,95%CI 1.34~2.15;P<0.00001),stroke severity(RR 1.70,95%CI 1.34~2.15;P<0.00001),and stroke risk.stroke severity(RR 1.53,95%CI 1.39~1.70;P<0.00001),NIHSS score(RR 2.91,95%CI 1.64~5.61;P=0.0003),early-onset epilepsy(RR 5.62,95%CI 5.08~6.22;P<0.00001),cortical lesions(RR 3.83.95%CI 2.23~6.58;P<0.00001),total anterior circulation infarction(RR 18.94,95%CI 10.38~34.57;P<0.00001),partial anterior circulation infarction(RR 4.39,95%CI 2.29~8.40;P<0.00001),cardiovascular events(RR 1.78,95%CI 1.59~1.99;P<0.00001).Conclusion:Based on a systematic review and meta-analysis,we developed a simple risk prediction model for late epilepsy in baseline ischemic stroke that integrates clinical risk factors,including infarct size,stroke recurrence,stroke etiology,stroke severity,NIHSS score,early onset epilepsy,cortical lesions,stroke subtype,and cardiovascular events.

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

A predictive model of the relationship between hematology,urine,clinical examination and the occurrence of depression risk are established based on machine learning

Background:Depression is a kind of emotional disorders caused by a variety of factors,with the accelerating pace of life,people in life and work facing competition pressure is increasing,the incidence of depression is increasing year by year,so the in-depth study of the pathogenesis of depression,and the development of depression risk prediction model is becoming increasingly important.Method:This study data is derived from the 2017–2018 follow-up data from the National Health and Nutrition Examination Survey database,a publicly available database using a multi-stage,hierarchical,clustered,probability sampling design to determine a nationally representative sample of non-institutionalized US civilians.Participants completed home interviews,laboratory measurements,and a physical examination.Details of the survey design have been published previously.This study evaluated the risk factors for the occurrence of depression from this study from multiple variables such as age,sex,and combined complications.Four machine learning algorithms(logistic regression,Lasso regression,support vector machine,random forest)were used to establish predictive classification models and compare the area under the subject operating feature curve and accuracy.The dataset was validated using a 10-fold cross-validation.Result:We excluded the invalid samples for 815 included samples,of which 570 cases were divided into the validation set and 245 cases were divided into the training set.The area under the curve(AUC)of Nomogram establishing risk of depression based on logistic regression was 0.73.Among the three machine learning models,the Lasso regression-based model AUC was 0.548,a mean AUC for support vector machines was 0.695,and a random forest AUC of 0.613.The support vector machines-based model predicted the best performance compared to other machine models.Conclusion:Random forest-based prediction models are able to assist clinicians in providing decision support when it is difficult to give an exact diagnosis.The model has good clinical utility and facilitates clinicians to identify high-risk patients and perform individualized treatment.The established four models of logistic regression,Lasso regression,support vector machine,and random forest all have good predictive power.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)

This work presents a comprehensive second-order predictive modeling (PM) methodology based on the maximum entropy (MaxEnt) principle for obtaining best-estimate mean values and correlations for model responses and parameters. This methodology is designated by the acronym 2nd-BERRU-PMP, where the attribute “2nd” indicates that this methodology incorporates second- order uncertainties (means and covariances) and second (and higher) order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best-Estimate Results with Reduced Uncertainties” and the last letter (“P”) in the acronym indicates “probabilistic,” referring to the MaxEnt probabilistic inclusion of the computational model responses. This is in contradistinction to the 2nd-BERRU-PMD methodology, which deterministically combines the computed model responses with the experimental information, as presented in the accompanying work (Part I). Although both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies yield expressions that include second (and higher) order sensitivities of responses to model parameters, the respective expressions for the predicted responses, for the calibrated predicted parameters and for their predicted uncertainties (covariances), are not identical to each other. Nevertheless, the results predicted by both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies encompass, as particular cases, the results produced by the extant data assimilation and data adjustment procedures, which rely on the minimization, in a least-square sense, of a user-defined functional meant to represent the discrepancies between measured and computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD)

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this methodology incorporates second-order uncertainties (means and covariances) and second-order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best- Estimate Results with Reduced Uncertainties” and the last letter (“D”) in the acronym indicates “deterministic,” referring to the deterministic inclusion of the computational model responses. The 2nd-BERRU-PMD methodology is fundamentally based on the maximum entropy (MaxEnt) principle. This principle is in contradistinction to the fundamental principle that underlies the extant data assimilation and/or adjustment procedures which minimize in a least-square sense a subjective user-defined functional which is meant to represent the discrepancies between measured and computed model responses. It is shown that the 2nd-BERRU-PMD methodology generalizes and extends current data assimilation and/or data adjustment procedures while overcoming the fundamental limitations of these procedures. In the accompanying work (Part II), the alternative framework for developing the “second- order MaxEnt predictive modelling methodology” is presented by incorporating probabilistically (as opposed to “deterministically”) the computed model responses.

Fourth-Order Predictive Modelling: II. 4th-BERRU-PM Methodology for Combining Measurements with Computations to Obtain Best-Estimate Results with Reduced Uncertainties

This work presents a comprehensive fourth-order predictive modeling (PM) methodology that uses the MaxEnt principle to incorporate fourth-order moments (means, covariances, skewness, kurtosis) of model parameters, computed and measured model responses, as well as fourth (and higher) order sensitivities of computed model responses to model parameters. This new methodology is designated by the acronym 4th-BERRU-PM, which stands for “fourth-order best-estimate results with reduced uncertainties.” The results predicted by the 4th-BERRU-PM incorporates, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizes the results produced by extant data assimilation and data adjustment procedures.

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2nd-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1st- and 2nd-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).

Analysis of influencing factors and the construction of predictive models for postpartum depression in older pregnant women

BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpartum depression(PPD)in older pregnant women.AIM To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.METHODS By adopting a cross-sectional survey research design,239 older pregnant women(≥35 years old)who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects.When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth,the Edinburgh PPD Scale(EPDS)was used to assess the presence of PPD symptoms.The women were divided into a PPD group and a no-PPD group.Two sets of data were collected for analysis,and a prediction model was constructed.The performance of the predictive model was evaluated using receiver operating characteristic(ROC)analysis and the Hosmer-Lemeshow goodness-of-fit test.RESULTS On the 42nd day after delivery,51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms.The incidence rate was 21.34%(51/239).There were statistically significant differences between the PPD group and the no-PPD group in terms of education level(P=0.004),family relationships(P=0.001),pregnancy complications(P=0.019),and mother–infant separation after birth(P=0.002).Multivariate logistic regression analysis showed that a high school education and below,poor family relationships,pregnancy complications,and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women(P<0.05).Based on the influencing factors,the following model equation was developed:Logit(P)=0.729×education level+0.942×family relationship+1.137×pregnancy complications+1.285×separation of the mother and infant after birth-6.671.The area under the ROC curve of this prediction model was 0.873(95%CI:0.821-0.924),the sensitivity was 0.871,and the specificity was 0.815.The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant(χ^(2)=2.749,P=0.638),indicating that the model did not show an overfitting phenomenon.CONCLUSION The risk of PPD among older pregnant women is influenced by educational level,family relationships,pregnancy complications,and the separation of the mother and baby after birth.A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.

Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer

BACKGROUND Transarterial chemoembolization(TACE)is the first-line treatment for patients with unresectable liver cancer;however,TACE is associated with postembolization pain.AIM To analyze the risk factors for acute abdominal pain after TACE and establish a predictive model for postembolization pain.METHODS From January 2018 to September 2018,all patients with liver cancer who underwent TACE at our hospital were included.General characteristics;clinical,imaging,and procedural data;and postembolization pain were analyzed.Postembolization pain was defined as acute moderate-to-severe abdominal pain within 24 h after TACE.Logistic regression and a classification and regression tree were used to develop a predictive model.Receiver operating characteristic curve analysis was used to examine the efficacy of the predictive model.RESULTS We analyzed 522 patients who underwent a total of 582 TACE procedures.Ninety-seven(16.70%)episodes of severe pain occurred.A predictive model built based on the dataset from classification and regression tree analysis identified known invasion of blood vessels as the strongest predictor of subsequent performance,followed by history of TACE,method of TACE,and history of abdominal pain after TACE.The area under the receiver operating characteristic curve was 0.736[95%confidence interval(CI):0.682-0.789],the sensitivity was 73.2%,the specificity was 65.6%,and the negative predictive value was 92.4%.Logistic regression produced similar results by identifying age[odds ratio(OR)=0.971;95%CI:0.951-0.992;P=0.007),history of TACE(OR=0.378;95%CI:0.189-0.757;P=0.007),history of abdominal pain after TACE(OR=6.288;95%CI:2.963-13.342;P<0.001),tumor size(OR=1.978;95%CI:1.175-3.330;P=0.01),multiple tumors(OR=2.164;95%CI:1.243-3.769;P=0.006),invasion of blood vessels(OR=1.756;95%CI:1.045-2.950;P=0.034),and TACE with drug-eluting beads(DEBTACE)(OR=2.05;95%CI:1.260-3.334;P=0.004)as independent predictive factors for postembolization pain.CONCLUSION Blood vessel invasion,TACE history,TACE with drug-eluting beads,and history of abdominal pain after TACE are predictors of acute moderate-to-severe pain.The predictive model may help medical staff to manage pain.

Multivariate predictive model for asymptomatic spontaneous bacterial peritonitis in patients with liver cirrhosis

BACKGROUNDSpontaneous bacterial peritonitis (SBP) is a detrimental infection of the asciticfluid in liver cirrhosis patients, with high mortality and morbidity. Earlydiagnosis and timely antibiotic administration have successfully decreased themortality rate to 20%-25%. However, many patients cannot be diagnosed in theearly stages due to the absence of classical SBP symptoms. Early diagnosis ofasymptomatic SBP remains a great challenge in the clinic.AIMTo establish a multivariate predictive model for early diagnosis of asymptomaticSBP using positive microbial cultures from liver cirrhosis patients with ascites.METHODSA total of 98 asymptomatic SBP patients and 98 ascites liver cirrhosis patients withnegative microbial cultures were included in the case and control groups,respectively. Multiple linear stepwise regression analysis was performed toidentify potential indicators for asymptomatic SBP diagnosis. The diagnosticperformance of the model was estimated using the receiver operatingcharacteristic curve.RESULTSPatients in the case group were more likely to have advanced disease stages,cirrhosis related-complications, worsened hematology and ascites, and higher mortality. Based on multivariate analysis, the predictive model was as follows: y (P) = 0.018 + 0.312 × MELD (model of end-stage liver disease) + 0.263 × PMN(ascites polymorphonuclear) + 0.184 × N (blood neutrophil percentage) + 0.233 ×HCC (hepatocellular carcinoma) + 0.189 × renal dysfunction. The area under thecurve value of the established model was 0.872, revealing its high diagnosticpotential. The diagnostic sensitivity was 73.5% (72/98), the specificity was 86.7%(85/98), and the diagnostic efficacy was 80.1%.CONCLUSIONOur predictive model is based on the MELD score, polymorphonuclear cells,blood N, hepatocellular carcinoma, and renal dysfunction. This model mayimprove the early diagnosis of asymptomatic SBP.

Predictive modeling of 30-day readmission risk of diabetes patients by logistic regression,artificial neural network,and EasyEnsemble

Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions.Classification of all-cause,30-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model performance.Results:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3 machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes.But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

Predictive models for characterizing the atomization process in pyrolysis of methyl ricinoleate

Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the product yield.Herein,predictive models to characterize the atomization process were developed.The effect of spray distance on Sauter mean diameter(SMD)of atomized MR droplets was examined,with the optimal spray distance to be 40-50 mm.Temperature mainly affected the physical properties of feedstock,with smaller droplet size obtained at increasing temperature.In addition,pressure had significant influence on SMD and higher pressure resulted in smaller atomized droplets.Then,a model for SMD prediction,combining temperature,pressure,spray distance,and structural parameters of nozzle,was developed through dimensionless analysis.The results showed that SMD was a power function of Reynolds number(Re),Ohnesorge number(Oh),and the ratio of spray distance to diameter of swirl chamber in the nozzle(H/dsc),with the exponents of-1.6618,-1.3205 and 0.1038,respectively.The experimental measured SMD was in good agreement with the calculated values,with the error within±15%.Moreover,the droplet size distribution was studied by establishing the relationship between the standard deviation of droplet size and SMD.This study could provide reference to the regulation and optimization of the atomization process in MR pyrolysis.

Clinical value of predictive models based on liver stiffness measurement in predicting liver reserve function of compensated chronic liver disease

BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.

Predictive Modeling for Growth and Enterotoxin Production of Staphylococcus aureus in Milk

Predictive microbiology was utilized to model Staphylococcus aureus(S. aureus) growth and staphylococcal enterotoxin A(SEA) production in milk in this study. The modified logistic model, modified Gompertz model and Baranyi model were applied to model growth data of S. aureus between 15℃ and 37℃. Model comparisons indicated that Baranyi model described the growth data more accurately than two others with a mean square error of 0.0129. Growth rates generated from Baranyi model matched the observed ones with a bias factor of 0.999 and an accuracy factor of 1.01, and fit a square root model with respect to temperature; other two modified models both overestimated the observed ones. SEA amount began to be detected when the cell number reached 10^(6.4) cfu · mL^(-1), and showed the linear correlation with time. Besides, the rate of SEA production fitted an exponential relationship as a function of temperature. Predictions based on the study could be applied to indicate possible growth of S. aureus and prevent the occurrence of staphylococcal food poisoning.