vascular disease

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


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


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


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


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


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


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 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


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.

Explaining Groups of Points in Low-Dimensional Representations Machine Learning

A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent. We treat this as an interpretable machine learning problem by leveraging the model that learned the low-dimensional representation to help identify the key differences between the groups. To solve this problem, we introduce a new type of explanation, a Global Counterfactual Explanation (GCE), and our algorithm, Transitive Global Translations (TGT), for computing GCEs. TGT identifies the differences between each pair of groups using compressed sensing but constrains those pairwise differences to be consistent among all of the groups. Empirically, we demonstrate that TGT is able to identify explanations that accurately explain the model while being relatively sparse, and that these explanations match real patterns in the data.