Cardiology/Vascular Diseases


Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression

Neural Information Processing Systems

This paper develops a prediction-based prescriptive model for optimal decision making that (i) predicts the outcome under each action using a robust nonlinear model, and (ii) adopts a randomized prescriptive policy determined by the predicted outcomes. The predictive model combines a new regularized regression technique, which was developed using Distributionally Robust Optimization (DRO) with an ambiguity set constructed from the Wasserstein metric, with the K-Nearest Neighbors (K-NN) regression, which helps to capture the nonlinearity embedded in the data. We show theoretical results that guarantee the out-of-sample performance of the predictive model, and prove the optimality of the randomized policy in terms of the expected true future outcome. We demonstrate the proposed methodology on a hypertension dataset, showing that our prescribed treatment leads to a larger reduction in the systolic blood pressure compared to a series of alternatives. A clinically meaningful threshold level used to activate the randomized policy is also derived under a sub-Gaussian assumption on the predicted outcome.


Safety, reliability crucial in AI development for ECG readings

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The use of artificial intelligence has been a hot topic in cardiology for the past few years. For example, deep neural networks can be used to analyze ECG tracings and may be more accurate than human experts. Even with these advantages, there may be some hesitation on completely relying on this technology. In a recent research letter published in Nature Medicine, researchers developed a way to integrate smoothed adversarial examples for single-lead ECGs. Researchers found that when subtle adversarial perturbations that are indistinguishable to the human eye were added to ECG tracings, the misdiagnosis rate of the deep learning algorithm was 74%.


AI helps scan heart disease patients to predict heart attacks

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Artificial intelligence has been used for the first time to instantly and accurately measure blood flow, in a study we part-funded. The results were found to be able to predict chances of death, heart attack and stroke, and can be used by doctors to help recommend treatments which could improve a patient's blood flow. Reduced blood flow, which is often treatable, is a common symptom of many heart conditions. International guidelines therefore recommend a number of assessments to measure a patient's blood flow, but many are invasive and carry a risk. Non-invasive blood flow assessments are available, including Cardiovascular Magnetic Resonance (CMR) imaging, but up until now, the scan images have been incredibly difficult to analyse in a manner precise enough to deliver a prognosis or recommend treatment.


LSTM-FCN for cardiology

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This algorithm consists of 2 parts: a LSTM block and a FCN part with 3 convolution layers. Long short-term memory recurrent neural networks are an improvement over the general recurrent neural networks,which possess a vanishing gradient problem. LSTM RNNs address the vanishing gradient problem commonly found in ordinary recurrent neural networks by incorporating gating functions into their state dynamics. For more information about LSTM networks, you can read this great article by Christopher Olah. In addition to the LSTM block, this part also includes a dimension shuffle.


Artificial intelligence gives stethoscopes a much-needed upgrade Berkeley Engineering

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Last month, the federal Food and Drug Administration (FDA) approved nearly half a dozen of their algorithms designed to detect heart murmurs and atrial fibrillation, irregular heartbeats that could lead to stroke or blood clots. And in December, the FDA granted a "breakthrough" device designation to an algorithm that analyzes data from the heart's electrical impulses for evidence of heart failure. Such a designation allows the agency to fast track significant innovations for approval.


Artificial Intelligence in Cardiovascular Magnetic Resonance Imaging - A joint

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Artificial Intelligence promises to revolutionize cardiovascular magnetic resonance in the near future, by offering until recently inconceivable new possibilities in acquisition, workflow and interpretation of images. Therefore, the EACVI and the SCMR decided to organize its first joint workshop on artificial intelligence in cardiovascular magnetic resonance imaging. The purpose of this workshop is to provide an overview about the current state of the art, recent progress, opportunities and future outlook of artificial intelligence in CMR. Recent developments will be illustrated by abstract sessions and industry showcases. We will all also cover current and future challenges, issues, as well as legal and regulatory issues of artificial intelligence in cardiovascular imaging. With this workshop we hope to enhance interaction between scientists, clinicians and companies involved in artificial intelligence research and applications, and foster the further evolution of this exciting new technology.


AI-Guided Ultrasound System from Caption Health Now Commercially Available in US

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Caption Health, a leading medical AI company, announced that its flagship product, Caption AI, the first AI-guided medical imaging acquisition system, is now available for pre-order by healthcare providers. Caption AI is a transformational new technology that enables healthcare practitioners--even those without prior ultrasound experience--with the ability to perform ultrasound exams quickly and accurately, by providing expert guidance, automated quality assessment, and intelligent interpretation capabilities. Caption AI comes equipped with Caption Guidance software, which uses artificial intelligence to provide real-time guidance and feedback on image quality to enable capture of diagnostic quality images. This announcement follows the recent groundbreaking marketing authorization of Caption Guidance software by the U.S. Food and Drug Administration (FDA). The safety and effectiveness of Caption Guidance was clinically validated in a multi-center prospective pivotal trial at Northwestern Medicine and Minneapolis Heart Institute at Allina Health with registered nurses with no prior ultrasound experience.


Model Assertions for Monitoring and Improving ML Models

arXiv.org Artificial Intelligence

ML models are increasingly deployed in settings with real world interactions such as vehicles, but unfortunately, these models can fail in systematic ways. To prevent errors, ML engineering teams monitor and continuously improve these models. We propose a new abstraction, model assertions, that adapts the classical use of program assertions as a way to monitor and improve ML models. Model assertions are arbitrary functions over a model's input and output that indicate when errors may be occurring, e.g., a function that triggers if an object rapidly changes its class in a video. We propose methods of using model assertions at all stages of ML system deployment, including runtime monitoring, validating labels, and continuously improving ML models. For runtime monitoring, we show that model assertions can find high confidence errors, where a model returns the wrong output with high confidence, which uncertainty-based monitoring techniques would not detect. For training, we propose two methods of using model assertions. First, we propose a bandit-based active learning algorithm that can sample from data flagged by assertions and show that it can reduce labeling costs by up to 40% over traditional uncertainty-based methods. Second, we propose an API for generating "consistency assertions" (e.g., the class change example) and weak labels for inputs where the consistency assertions fail, and show that these weak labels can improve relative model quality by up to 46%. We evaluate model assertions on four real-world tasks with video, LIDAR, and ECG data.


Heart Disease Detection Using Python And Machine Learning

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Python is a dynamic modern object -oriented programming language It is easy to learn and can be used to do a lot of things both big and small Python is what is referred to as a high level language Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps! SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily If you want to learn to code, Python GUIs are the best way to start! I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. The Python coding language integrates well with other platforms – and runs on virtually all modern devices.


Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

arXiv.org Machine Learning

It is a truth universally acknowledged that an observed association without known mechanism must be in want of a causal estimate. However, causal estimation from observational data often relies on the (untestable) assumption of `no unobserved confounding'. Violations of this assumption can induce bias in effect estimates. In principle, such bias could invalidate or reverse the conclusions of a study. However, in some cases, we might hope that the influence of unobserved confounders is weak relative to a `large' estimated effect, so the qualitative conclusions are robust to bias from unobserved confounding. The purpose of this paper is to develop \emph{Austen plots}, a sensitivity analysis tool to aid such judgments by making it easier to reason about potential bias induced by unobserved confounding. We formalize confounding strength in terms of how strongly the confounder influences treatment assignment and outcome. For a target level of bias, an Austen plot shows the minimum values of treatment and outcome influence required to induce that level of bias. Domain experts can then make subjective judgments about whether such strong confounders are plausible. To aid this judgment, the Austen plot additionally displays the estimated influence strength of (groups of) the observed covariates. Austen plots generalize the classic sensitivity analysis approach of Imbens [Imb03]. Critically, Austen plots allow any approach for modeling the observed data and producing the initial estimate. We illustrate the tool by assessing biases for several real causal inference problems, using a variety of machine learning approaches for the initial data analysis. Code is available at https://github.com/anishazaveri/austen_plots