Deep Learning for Cardiovascular Risk Stratification

Schlesinger, Daphne E.; Stultz, Collin M.

doi:10.1007/s11936-020-00814-0

Cited by 19 publications

(17 citation statements)

References 54 publications

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“…A driving principle of this work was that a successful clinical risk model is not only determined by its performance but also by its ease of use. 8 Although modern EHR systems can implement risk models that use an arbitrary number of features, such systems are not available in all clinical settings. 18…”

Section: Discussionmentioning

confidence: 99%

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score

et al. 2022

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ObjectiveTo use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML).MethodsIn 1130 patients with moderate or severe AS, we used bootstrap lasso regression (BLR), an ML method, to identify echocardiographic and clinical features important for predicting the combined outcome of all-cause mortality or aortic valve replacement (AVR) within 5 years after the initial echocardiogram. A separate hold out set, from a different centre (n=540), was used to test the generality of the model. We also evaluated model performance with respect to each outcome separately and in different subgroups, including patients with LGAS.ResultsOut of 69 available variables, 26 features were identified as predictive by BLR and expert knowledge was used to further reduce this set to 9 easily available and input features without loss of efficacy. A ridge logistic regression model constructed using these features had an area under the receiver operating characteristic curve (AUC) of 0.74 for the combined outcome of mortality/AVR. The model reliably identified patients at high risk of death in years 2–5 (HRs ≥2.0, upper vs other quartiles, for years 2–5, p<0.05, p=not significant in year 1) and was also predictive in the cohort with LGAS (n=383, HRs≥3.3, p<0.05). The model performed similarly well in the independent hold out set (AUC 0.78, HR ≥2.5 in years 1–5, p<0.05).ConclusionIn two separate longitudinal databases, ML identified prognostic features and produced an algorithm that predicts outcome for up to 5 years of follow-up in patients with AS, including patients with LGAS. Our algorithm, the Aortic Stenosis Risk (ASteRisk) score, is available online for public use.

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Section: Discussionmentioning

confidence: 99%

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score

et al. 2022

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“…This is remarkable as in both analytical strategies, overfit was thought to be controlled by reducing the number of predictors at smaller data availabilities (MLNN) or by cross-validation (XGBoost). Another aspect of our study was to demonstrate how the black box of ML models can be opened to explain why a model arrives at a particular result [ 35 ]. Lack of explainability of an ML model may be an important obstacle to its bedside use, in particular if consistence with clinical expertise is unclear.…”

Section: Discussionmentioning

confidence: 99%

The roles of predictors in cardiovascular risk models - a question of modeling culture?

Wallisch

Agibetov

Dunkler

et al. 2021

BMC Med Res Methodol

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Background While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations. Methods We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots. Results When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models. Conclusion Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm.

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“…This algorithm enables having different learning rates for different parameters alongside automatic tuning of learning rate. However, it is expensive computationally and has a decreasing learning rate resulting in slower training [101].…”

Section: ) Optimizersmentioning

confidence: 99%

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

et al. 2021

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Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

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Deep Learning for Cardiovascular Risk Stratification

Cited by 19 publications

References 54 publications

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score

The roles of predictors in cardiovascular risk models - a question of modeling culture?

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

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