heart failure


Machine-learning derived model can help predict risk of heart failure for diabetes patients

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Heart failure is an important potential complication of type 2 diabetes that occurs frequently and can lead to death or disability. Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place. However, a way of accurately identifying which diabetes patients are most at risk for heart failure remains elusive. A new study led by investigators from Brigham and Women's Hospital and UT Southwestern Medical Center unveils a new, machine-learning derived model that can predict, with a high degree of accuracy, future heart failure among patients with diabetes.


Predicting risk of heart failure for diabetes patients with help from machine learning

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Heart failure is an important potential complication of type 2 diabetes that occurs frequently and can lead to death or disability. Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place. However, a way of accurately identifying which diabetes patients are most at risk for heart failure remains elusive. A new study led by investigators from Brigham and Women's Hospital and UT Southwestern Medical Center unveils a new, machine-learning derived model that can predict, with a high degree of accuracy, future heart failure among patients with diabetes.


New AI neural network approach detects heart failure from a single heartbeat with 100% accuracy

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Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 percent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat, a new study reports. Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes. Dr. Sebastiano Massaro, associate professor of organizational neuroscience at the University of Surrey, has worked with colleagues Mihaela Porumb and Dr. Leandro Pecchia at the University of Warwick and Ernesto Iadanza at the University of Florence, to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognizing patterns and structures in data. Published in the Biomedical Signal Processing and Control Journal, their research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors.


AI detects heart failure with 100% accuracy - Express Computer

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With the help of Artificial Intelligence(AI), researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 per cent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat. Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes. The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data. "We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100 per cent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure," said study researcher Sebastiano Massaro, Associate Professor at the University of Surrey in the UK.


Novel AI system proves 100% accurate at detecting heart failure from a single heartbeat

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Nearly 10 percent of adults over the age of 65 suffer from some kind of congestive heart failure (CHF). There are a variety of different causes for CHF but the fundamental chronic condition generally results from the heart being unable to pump blood effectively through the body. X-rays, blood tests, and ultrasounds all offer clinicians useful ways to diagnose CHF, but one of the more common methods involves using electrocardiogram (ECG) signals to determine heart rate variability over a number of minutes, or even multiple measurements over days. An impressive new approach has now been demonstrated, using a convolutional neural network (CNN) that can identify CHF nearly instantly by checking ECG data from just one heartbeat. "We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts," says Sebastian Massaro, from the University of Surrey.


Deep learning AI may identify atrial fibrillation from a normal rhythm ECG - Times of India

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Although early and requiring further research before implementation, the findings could aid doctors investigating unexplained strokes or heart failure, enabling appropriate treatment. Researchers have trained an artificial intelligence model to detect the signature of atrial fibrillation in 10-second electrocardiograms (ECG) taken from patients in normal rhythm. The study, involving almost 181,000 patients and published in The Lancet, is the first to use deep learning to identify patients with potentially undetected atrial fibrillation and had an overall accuracy of 83%. Atrial fibrillation is estimated to affect 2.7–6.1 million people in the United States and is associated with increased risk of stroke, heart failure and mortality. It is difficult to detect on a single ECG because patients' hearts can go in and out of this abnormal rhythm, so atrial fibrillation often goes undiagnosed.


How artificial intelligence can detect hidden diseases

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What if technology could predict a hereditary disease you could stop from progressing? What if a visit to your primary physician for carpal tunnel syndrome ended with a suggestion to get tested for a rare illness? As Komodo Health's artificial intelligence algorithms crunch a decade of data about health conditions across several hundred million Americans, many what-if scenarios are becoming pathways for the next clinical assessment to be taken. Founded in 2014, Komodo, funded by Felicis Ventures and McKesson Ventures, has mapped out 300 million individual health identities across the country to find patterns signaling the presence of disease, years before they're ever diagnosed. At a time when chronic conditions account for 75 percent of the $3 trillion US healthcare spend annually, identifying when symptoms occur earliest or recognizing patterns of activity that are often a precursor to the manifestation of diseases is vital in preventing those economically, physically and mentally crippling illnesses to either exist or progress.


'Computers better than doctors for predicting heart failure'

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Computers are better than doctors at predicting which patients are most at risk from heart failure, London researchers revealed today. Machines using artificial intelligence were right on 75 per cent of occasions, compared with 59 per cent achieved by clinicians. The world-first breakthrough could pave the way for tailor-made care for the 920,000 Britons with heart failure, leading to improved survival rates by better determining who requires surgery and who can be treated with tablets. The findings came as a Department of Health review called for NHS staff, overstretched as a result of 100,000 vacancies, to become more "digital savvy" and embrace technology to improve patient care. Today's research, led by a team at Imperial College London, used computer programme 4Dsurvival to analyse historic MRI heart scans from 302 people with pulmonary hypertension.


Artificial intelligence predicts outcomes for heart patients

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For the first time researchers that we part-fund have used Artificial Intelligence to predict outcomes for heart patients using MRI scans, enabling doctors to find the best treatments for individual patients. The computer program, called 4Dsurvival, takes each patient's scan and tracks the motion of the heart at hundreds of points every second. From these 3D pictures of the moving heart the machine learns to predict the risk of dying from heart failure. So far, the team at Imperial College London and the Medical Research Council have used the technology to predict the prognosis for 302 people with a heart condition called pulmonary hypertension (PH) a rare but serious condition, which damages the arteries in the lungs, and can be fatal. The technology outperformed doctors, being able to correctly predict a patient's prognosis 75% of the time.


Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset

arXiv.org Machine Learning

In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216,394 patients. We believe this to be the largest study in CHF onset prediction with respect to the number of CHF case patients in the cohort and the test set (3,332 CHF patients) on which the AUC metrics are reported. We explore the extent to which LSTM (Long Short Term Memory) based model, a variant of RNNs, can accurately predict the onset of CHF when compared to known linear baselines like Logistic Regression, Random Forests and deep learning based models such as Multi-Layer Perceptron and Convolutional Neural Networks. We utilize demographics, medical diagnosis and procedure data from 21,405 CHF and 194,989 control patients to as our features. We describe our feature embedding strategy for medical diagnosis codes that accommodates the sparse, irregular, longitudinal, and high-dimensional characteristics of EHR data. We empirically show that LSTMs can capture the longitudinal aspects of EHR data better than the proposed baselines. As an attempt to interpret the model, we present a temporal data analysis-based technique on false positives to attribute feature importance. A model capable of predicting the onset of congestive heart failure months in the future with this level of accuracy and precision can support efforts of practitioners to implement risk factor reduction strategies and researchers to begin to systematically evaluate interventions to potentially delay or avert development of the disease with high mortality, morbidity and significant costs.