Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation
@article{Mamprin2020GradientBO, title={Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation}, author={Marco Mamprin and Jo M. Zelis and Pim A. L. Tonino and Svitlana Zinger and Peter H. N. de With}, journal={Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology}, year={2020} }
Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. Statistical predictors are not robust enough to correctly identify patients who would benefit from Transcatheter Aortic Valve Implantation (TAVI). This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI. We adopt a modern gradient boosting on decision trees algorithm, specifically designed for categorical features. In…
7 Citations
Inter-Center Cross-Validation and Finetuning without Patient Data Sharing for Predicting Transcatheter Aortic Valve Implantation Outcome
- Computer Science2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
- 2020
This research demonstrates that the predicting capabilities of deep learning models can be extended to and cross-validated with other centers, independent of limitations in data-sharing policies, and shows that finetuning can be exploited to considerably improve the accuracy of the prediction models.
Feature Selection and Prediction of Heart diseases using Gradient Boosting Algorithms
- Computer Science2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)
- 2021
feature importance ranking of two gradient boosting algorithms XGBoost and CatBoost were computed on Cleveland, Statlog heart and SA heart data sets and the classifiers exhibited improved performance on selected features when compared to their performance on all features.
Finding improved predictive models with Generalized Boosted Models on Hungarian Myocardial Infarction Registry
- Computer Science2020 IEEE 20th International Symposium on Computational Intelligence and Informatics (CINTI)
- 2020
New predictive modelling results achieved with Generalized Boosted Models (GBM) on the dataset of Hungarian Myocardial Infarction Registry are presented, representing a strong and stable learner with almost the similar predictive power as the previously published random forest models.
A review of machine learning for cardiology.
- Computer ScienceMinerva cardiology and angiology
- 2021
This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field, and discusses the main open issues in applying Machine Learning tools to cardiology tasks.
Identification of patients at risk of cardiac conduction diseases requiring a permanent pacemaker following TAVI procedure: a deep-learning approach on ECG signals
- Medicine2022 12th International Conference on Biomedical Engineering and Technology (ICBET)
- 2022
This study confirms that it is possible to identify and stratify even further, the risk for patients that will develop CCD after TAVI, by using the information available in the ECG data immediately following the procedure.
Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes
- Medicine, Computer ScienceBMC Medical Informatics and Decision Making
- 2022
The results indicate that machine learning methods can be used to exploit the predictive value ofPROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power.
Developing a Novel Continuous Metabolic Syndrome Score: A Data Mining Based Model
- Computer Science
- 2021
The research results show that high TG and central obesity have the greatest impact on MetS and FBS has no effect on the final prognosis and in the preliminary stages of MetS, WC, HDL and SBP are the most important influencing factors that play an important role in forecasting.
References
SHOWING 1-10 OF 18 REFERENCES
Prediction of Poor Outcome After Transcatheter Aortic Valve Replacement.
- MedicineJournal of the American College of Cardiology
- 2016
Value of the "TAVI2-SCORe" versus surgical risk scores for prediction of one year mortality in 511 patients who underwent transcatheter aortic valve implantation.
- MedicineThe American journal of cardiology
- 2015
EuroSCORE II and STS as mortality predictors in patients undergoing TAVI.
- MedicineRevista da Associacao Medica Brasileira
- 2016
In this cohort, the STS and EuroSCORE II were predictors of in-hospital and 30-days mortality in patients with severe aortic stenosis undergoing TAVI.
EuroSCORE II.
- MedicineEuropean journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
- 2012
Cardiac surgical mortality has significantly reduced in the last 15 years despite older and sicker patients, and EuroSCORE II is better calibrated than the original model yet preserves powerful discrimination.
Addressing the Class Imbalance Problem in Medical Datasets
- Computer Science
- 2013
This paper examines the performance of over-sampled and under-sampling techniques to balance cardiovascular data and proposes an improved under sampling technique that displays significant better performance than the existing methods.
A Unified Approach to Interpreting Model Predictions
- Computer ScienceNIPS
- 2017
A unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations), which unifies six existing methods and presents new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
Random Forests
- Computer ScienceMachine Learning
- 2004
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Consistent Individualized Feature Attribution for Tree Ensembles
- Computer ScienceArXiv
- 2018
This work develops fast exact tree solutions for SHAP (SHapley Additive exPlanation) values, which are the unique consistent and locally accurate attribution values, and proposes a rich visualization of individualized feature attributions that improves over classic attribution summaries and partial dependence plots, and a unique "supervised" clustering.
XGBoost: A Scalable Tree Boosting System
- Computer ScienceKDD
- 2016
This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
CatBoost: unbiased boosting with categorical features
- Computer ScienceNeurIPS
- 2018
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit and provides a detailed analysis of this problem and demonstrates that proposed algorithms solve it effectively, leading to excellent empirical results.