Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

New normalized nonlocal hybrid level set method for image segmentation

This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.

Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model＇s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.

Red blood cell distribution width improves the prediction of 28-daymortality for patients with sepsis-induced acute kidney injury:A retrospective analysis from MIMIC-IV database usingpropensity score matching

Background:The predictive value of red blood cell distribution width(RDW)for mortality in patients withsepsis-induced acute kidney injury(SI-AKI)remains unclear.The present study aimed to investigate the potentialassociation between RDW at admission and outcomes in patients with SI-AKI.Methods:The Medical Information Mart for Intensive Care(MIMIC)-IV(version 2.0)database,released in Juneof 2022,provides medical data of SI-AKI patients to conduct our related research.Based on propensity scorematching(PSM)method,the main risk factors associated with mortality in SI-AKI were evaluated using Coxproportional hazards regression analysis to construct a predictive nomogram.The concordance index(C-index)and decision curve analysis were used to validate the predictive ability and clinical utility of this model.Patientswith SI-AKI were classified into the high-and low-RDW groups according to the best cut-off value obtained bycalculating the maximum value of the Youden index.Results:A total of 7574 patients with SI-AKI were identified according to the filter criteria.Compared withthe low-RDW group,the high-RDW group had higher 28-day(9.49%vs.31.40%,respectively,P<0.001)and7-day(3.96%vs.13.93%,respectively,P<0.001)mortality rates.Patients in the high-RDW group were moreprone to AKI progression than those in the low-RDW group(20.80%vs.13.60%,respectively,P<0.001).Basedon matched patients,we developed a nomogram model that included age,white blood cells,RDW,combinedhypertension and presence of a malignant tumor,treatment with vasopressor,dialysis,and invasive ventilation,sequential organ failure assessment,and AKI stages.The C-index for predicting the probability of 28-day survivalwas 0.799.Decision curve analysis revealed that the model with RDW offered greater net benefit than that withoutRDW.Conclusion:The present findings demonstrated the importance of RDW,which improved the predictive ability ofthe nomogram model for the probability of survival in patients with SI-AKI.

Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment(SOFA)scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care(MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.Methods:A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database.Group-based trajectory modeling(GBTM)was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit(ICU).The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.Results:A total of 16,743 patients with sepsis were included in the cohort.The median survival age was 66 years(interquartile range:54-76 years).The 7-day and 28-day in-hospital mortality were 6.0%and 17.6%,respectively.Five different trajectories of SOFA scores according to the model fitting standard were determined:group 1(32.8%),group 2(30.0%),group 3(17.6%),group 4(14.0%)and group 5(5.7%).Univariate and multivariate Cox regression analyses showed that,for different clinical outcomes,trajectory group 1 was used as the reference,while trajectory groups 2-5 were all risk factors associated with the outcome(P<0.001).Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients’SOFA scores(P<0.05).Conclusion:This approach may help identify various groups of patients with sepsis,who may be at different levels of risk for adverse health outcomes,and provide subgroups with clinical importance.