首页|University Teaching Hospital Researcher Reports on Findings in Machine Learning (Predictive Machine Learning Model For Mechanical Dilatation in Transvenous Lead Extraction Procedures)
University Teaching Hospital Researcher Reports on Findings in Machine Learning (Predictive Machine Learning Model For Mechanical Dilatation in Transvenous Lead Extraction Procedures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om the University Teaching Hospital by NewsRx correspondents, research stated, “ Transvenous lead extraction (TLE) remains a procedure that requires a high level of expertise, with a doubled risk of death and clinical failure when performed in low-volume centers compared to highvolume ones. The aim of this study was to create a machine learning (ML)-based risk stratification system for predicting the need for mechanical dilatation in patients undergoing TLE due to infection.” The news reporters obtained a quote from the research from University Teaching H ospital: “We designed a ML-based risk stratification system trained with data fr om our registry to predict the need for mechanical dilatation in patients underg oing TLE for infection. An extensive evaluation of 5 different ML models (k-near est neighbors, support vector machine, decision tree, and decision tree ensemble s, such as random forest and gradient boosting machine) was conducted to identif y a classifier with the highest potential to correctly predict previously unseen patients. Data to train the model was extracted from our 25-year registry of pa tients undergoing TLE (June 1998 - March 2023), for a total of 491 patients (77. 8% male; age 69.7 ± 12.8 years) and 938 leads (ICD 21.2% ; pacing 78.8%; indwelling time 61 ± 60 months) removed with succes s in 100% of cases. Each patient was represented by a set of 21 at tributes (14 clinical, 7 device-related). Manual traction (MT) was used in 27.5% of cases, and mechanical dilatation (MD) was employed in the remaining 72.5% of cases. 5-fold nested cross validation was used to estimate performances: in t urn, 393 patients were used for training and model selection, and 98 patients we re used for independent testing. According to the evaluation, Gradient Boosting Machine performed best, achieving test accuracy of 89% (+/- 2% std. dev.), test sensitivity of 95% (+/- 3% std. dev .), test specificity of 73% (+/- 8% std. dev.), test AUROC of 92% (+/- 1% std. dev.). A further interpre tability analysis on the best performing decision tree was conducted, showing re markable adherence between the internal decisions taken by the model to make pre dictions and the current clinical practice for TLE.”
University Teaching HospitalCyborgsE merging TechnologiesMachine LearningRisk and Prevention
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