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LASSO regression coefficients on the different penalty parameters. LASSO: least absolute shrinkage and selection operator.
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Background
Cancer indeed represents a significant public health challenge, and unplanned extubation of peripherally inserted central catheter (PICC-UE) is a critical concern in patient safety. Identifying independent risk factors and implementing high-quality assessment tools for early detection in high-risk populations can play a crucial role in r...
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Context 1
... Figure 2, each colored line represents a variable trend that decreases as the penalty factor λ changes, resulting in the model incorporating fewer variables. In Figure 3, the dashed line on the left indicates the λ value associated with the maximum AUC and the number of features included in the model. On the right, the dashed line represents a reduction in the number of features in the model as the standard error increases by 1 to achieve the maximum AUC. ...
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Citations
... Machine learning (ML), a subset of artificial intelligence, enables computers to learn from data and identify patterns without explicit programming 10 .Compared to traditional statistical methods, greater flexibility and adaptability are offered by ML algorithms, enabling the handling of complex and nonlinear relationships within the data. In recent years, various diseases and complications have been successfully predicted using ML models, which demonstrate excellent performance and clinical utility 11,12 . Despite growing interest in ML applications in healthcare, limited research has been conducted on its use in predicting the risk of UTIs following urostomy. ...
This research aims to design and validate a machine learning model to predict the probability of urinary tract infections within 90 days post-urostomy in bladder cancer patients. Clinical and follow-up information from 317 patients who had urostomy procedures at the First Affiliated Hospital of Shanxi Medical University (May 2018–May 2024) were analyzed. The dataset were partitioned into training and testing sets, and feature selection was executed via the Least Absolute Shrinkage and Selection Operator regression technique. Seven machine learning algorithms were employed: Logistic Regression, K-Nearest Neighbors, LightGBM, Random Forest, XGBoost, Support Vector Machine, and Multi-Layer Perceptron. Performance metrics for the model were assessed using multiple evaluation indicators, including AUC, accuracy, sensitivity, specificity, Positive Predictive Value, Negative Predictive Value, and F1 score. SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations algorithms were applied for model interpretability. UTIs occurred in 22.08% of patients within 90 days after urostomy. The predictive model pinpointed eight important clinical features. Among the developed models, the SVM model demonstrated the best overall performance with AUC (0.835), accuracy (0.825), precision (0.583), recall (0.778), and F1 score (0.667). This model, designed to assess UTIs risk after urostomy in bladder cancer patients, has been deployed online for healthcare professionals at: https://zqmodel.shinyapps.io/shinydashboard_model/.
... In further research, we want to add methods like hierarchical cluster analysis [75][76][77][78] or multivariate analysis [79][80][81][82] that could provide deeper insights and improve the robustness of our results by revealing patterns and relationships that could be undetected in the currently used framework of mathematical-statistical methods. We want to pay more attention to the selection of the relevant criteria, the selection of the method for determining their importance, as well as the selection of the MCDM method itself. ...
Municipal activities should not be profitable. Their intention is to provide the highest possible quality of service to citizens and, in this way, contribute to improving their quality of life. For this reason, the evaluation of their performance is very complex and should include several aspects, or criteria. The aim of this study is to quantify the agreement of the financial health assessment of the territorial self-government entities in 2020 with the financial health assessment based on a gradually decreasing number of entry criteria. For this purpose, we use a TOPSIS technique, and a total of 26 combinations of criteria are created with a gradually decreasing number of criteria, i.e., five, four, three, and two criteria used. For a description of the results obtained, we use a wide range of mathematical and statistical methods. The tests used include the Jaccard index, Kolmogorov–Smirnov test, Levene test, Moran index, and others. Our results confirm the fact that the outcome of MCDM analysis is directly and significantly affected by the structure and number of entry criteria. The reduction in the number of criteria resulted in a change in the parameters of the overall results.
... Seven standard machine learning algorithms: Naïve Bayes (nb), AdaBoost Classification Tree (adaboost), cancerclass, Random Forest (rf), Boost Logistic Regression (LogiBoost), Weighted k-Nearest Neighbor Classifier (kknn), and Support Vector Machines with Class Weights (svmRadialWeights), were implemented to create a model for the prediction of a binary variable [16][17][18][19][20][21]. The detailed parameters of each are shown in the original code and Supplementary Table S4. ...
The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients’ outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).
Comparing the differences in efficacy and adverse reactions on the application between tunnel peripherally inserted central catheter (TPICC) and ultrasound-guided peripherally inserted central catheter (PICC) in patients with advanced tumors. A retrospective investigation was conducted to collect treatment data. We randomly selected 200 patients with advanced tumors who were admitted to our hospital from January 2020 to January 2022 as the research subjects. The observation group consisted of 100 cases of tunnel PICC catheterization, while the control group consisted of 100 cases of PICC catheterization. We observed and compared the catheterization time, PICC puncture success rate, intraoperative blood loss, pain degree, comfort level, and postoperative complication rate between the 2 groups. Compared with the control group, there was no significant difference at the distribution of PICC indwelling time in the observation group, and the difference was not statistically significant (P > .05). The success rate on PICC puncture in the observation group was higher than that on the control group (P < .05). Intraoperative bleeding volume and numerical rating scale (NRS) of the observation group were lower than those of the control group (P < .05). At 1 month postoperatively, comfort ratings in the observation group and the control group were lower than those of their same groups at 1 week postoperatively (P < .05); At 1 week and 1 month postoperatively, comfort rating in the observation group were lower than that in the control group (P < .05). The incidence of postoperative complication in the observation group was lower than that in the control group (P < .05). TPICC improves the success rate on puncture and the post-catheterization comfort, it reduces NRS and the risks on complications, which owns high safety.
