Singapore's Health Sciences Authority has approved the use of an artificial intelligence-powered (AI) software for the automated analysis and reporting of vascular ultrasound scans. Developed by See-Mode Technologies, the application taps deep learning, text recognition, and signal processing technologies with the aim of helping clinicians interpret such images -- a task that typically is performed manually, time-consuming, and error-prone. Such scans, used for patients with cardiovascular or heart diseases, are commonly analysed by a sonographer or radiologist who has to manually review between 50 and 150 images for each patient, according to See-Mode. No system is infallible and cybersecurity breaches are inevitable, but Singapore needs to do better in mitigating the risks and following through on its pledge to safeguard citizen data. "The end result is a hand-written, paper-based template filled with drawings, numbers, and measurements, which can take as long as 20 minutes per patient for severe cases," it said in a statement Tuesday.
The objective of this usecase is to predict the major risk factors that may result in the development of heart diseases in the future by using machine learning algorithms. Heart diseases are one of the main causes of death worldwide. Many factors like age, family history, high BP, high cholesterol levels, unhealthy lifestyles, lack of physical activity, etc can be attributed to the increasing cases of cardiovascular diseases. While some of these factors can be controlled, some others like age, hereditary, etc cannot be controlled by individuals. These factors are not constant among the individuals and keep varying.
Artificial intelligence is rather hard to define, if only because researchers themselves do not agree on what that concept should or should not include. But one thing is certain: when you hear about recent advances in artificial intelligence, more likely than not, it is related to the amazing advances made in one particular field of artificial intelligence, namely supervised learning. Imagine that you are a cardiologist and that you have to predict the risk of recurrence of a patient who has just had a heart problem. You will look at his sex, his age, his weight, his blood pressure, his lifestyle, his family history, etc., then you will make a prediction. You could ask a mathematical model to make that prediction on your behalf.
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.
You are free to share this article under the Attribution 4.0 International license. An experimental device uses machine learning tools--and a bathroom scale--to monitor heart failure. Researchers envision this scenario: The user steps onto a the scale and touches metal pads. The device records an electrocardiogram from their fingers and--more importantly--circulation pulsing that makes the body subtly bob up and down on the scale. Machine learning tools compute that heart failure symptoms have worsened.
BioSig Technologies, Inc. (NASDAQ: BSGM) ("BioSig" or the "Company"), a medical technology company developing a proprietary biomedical signal processing platform designed to improve signal fidelity and uncover the full range of ECG and intra-cardiac signals, today announced that the Company entered into a technical collaboration with Reified Capital, a provider of advanced artificial intelligence-focused technical advisory services to the private sector. Reified was co-founded by Dr. Alexander D. Wissner-Gross and Timothy M. Sullivan, the founders of Gemedy. The new collaboration with Cambridge, Massachusetts-based Reified will focus on developing a foundational artificial intelligence platform on the basis of integrated healthcare datasets, beginning with ECG and EEG data acquired by BioSig's first product, PURE EP(tm) System - a novel real-time signal processing platform engineered to provide electrophysiologists with high fidelity cardiac signals. Electrophysiology focused technological solutions developed under the terms of this collaboration will be integrated into the PURE EP(tm) technology platform. Reified is led by Harvard- and MIT-trained computer scientist and physicist Dr. Wissner-Gross, an award-winning computer scientist, physicist, entrepreneur and author.
The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia.