Cardiology/Vascular Diseases


Introduction To Artificial Intelligence -- Neural Networks

#artificialintelligence

Inspired by the structure of the brain, artificial neural networks (ANN) are the answer to making computers more human like and help machines reason more like humans. They are based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. To understand how artificial neural networks work let's first briefly look at the human ones. The exact workings of the human brain are still a mystery. Yet, some aspects of this amazing processor are known. In particular, the most basic element of the human brain is a specific type of cell which, unlike the rest of the body, doesn't appear to regenerate.


Netflix-style algorithm can detect who will DIE from a heart attack with 90 per cent accuracy

Daily Mail - Science & tech

Algorithms similar to those employed by Netflix and Spotify to customise services are now better than human doctors at spotting who will die or have a heart attack. Machine learning was used to train LogitBoost, which its developers say can predict death or heart attacks with 90 per cent accuracy. It was programmed to use 85 variables to calculate the risk to the health of the 950 patients that it was fed scans and data from. Patients complaining of chest pain were subjected to a host of scans and tests before being treated by traditional methods. Their data was later used to train the algorithm.


Scientists teach computers fear--to make them better drivers

#artificialintelligence

NEW ORLEANS, LOUISIANA--Computers can master some tasks--like playing a game of Go--through trial and error. But what works for a game doesn't work for risky real-world tasks like driving a car, where "losing" might involve a high-speed collision. To drive safely, humans have an exquisite feedback system: our fight-or-flight response, in which physiological reactions like a rapid heart rate and sweaty palms signal "fear," and so keep us vigilant and, theoretically, out of trouble. Now, researchers at Microsoft are giving artificial intelligence (AI) programs a rough analog of anxiety to help them sense when they're pushing their luck. The scientists placed sensors on people's fingers to record pulse amplitude while they were in a driving simulator, as a measure of arousal.


Scientists teach computers fear--to make them better drivers

#artificialintelligence

NEW ORLEANS, LOUISIANA--Computers can master some tasks--like playing a game of Go--through trial and error. But what works for a game doesn't work for risky real-world tasks like driving a car, where "losing" might involve a high-speed collision. To drive safely, humans have an exquisite feedback system: our fight-or-flight response, in which physiological reactions like a rapid heart rate and sweaty palms signal "fear," and so keep us vigilant and, theoretically, out of trouble. Now, researchers at Microsoft are giving artificial intelligence (AI) programs a rough analog of anxiety to help them sense when they're pushing their luck. The scientists placed sensors on people's fingers to record pulse amplitude while they were in a driving simulator, as a measure of arousal.


VIDEO: Artificial intelligence could enhance cardiac imaging, AF detection

#artificialintelligence

In this video exclusive, Mark J. Day, PhD, MBA, MS, BSc, discusses the benefits of artificial intelligence and how its impact on medical imaging can be vital to cardiologists. Day, the executive vice president, research & development for iRhythm Technologies, Inc., said, the greatest potential impact for AI is in detecting AF. "We know right now that there's on the order of around 1 million patients in the U.S. alone walking around without understanding that they have AF," Day said. "There's a considerable stroke risk with that population, the problem being that they're spread across the entire population. The reality is with that prevalence in the population, we need technologies that are very capable of interpreting very large amounts of data and being very accurate." Day highlighted topics related to findings based on a study published in Nature Medicine on cardiologist-level arrhythmia detection and the use of computerized ECG.


As Artificial Intelligence Moves Into Medicine, The Human Touch Could Be A Casualty

NPR Technology

When Kim Hilliard shows up at the clinic at the New Orleans University Medical Center, she's not there simply for an eye exam. The human touches she gets along the way help her navigate her complicated medical conditions. In addition to diabetes, the 56-year-old has high blood pressure. She has also had back surgery and has undergone bariatric surgery to help her control her weight. Hilliard is also at risk of blindness, which can result from a condition called diabetic retinopathy.


Impact of Argument Type and Concerns in Argumentation with a Chatbot

arXiv.org Artificial Intelligence

Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.) However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive.


Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)

arXiv.org Machine Learning

Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies and have occasionally missing information due to the imperfections in equipment or patient circumstances. Researchers have handled each of these issues differently, often handling missing data through mean value imputation and then using sequence models over the multivariate signals while ignoring the different resolution of signals. We propose a unified model named Multi-resolution Flexible Irregular Time series Network (Multi-FIT). The building block for Multi-FIT is the FIT network. The FIT network creates an informative dense representation at each time step using signal information such as last observed value, time difference since the last observed time stamp and overall mean for the signal. Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal. The multi-FIT model uses multiple FIT networks for sets of signals with different resolutions, further facilitating the construction of flexible representations. Our model has three main contributions: a.) it does not impute values but rather creates informative representations to provide flexibility to the model for creating task-specific representations b.) it models the relationship between different signals in the form of support signals c.) it models different resolutions in parallel before merging them for the final prediction task. The FIT, FIT-V and Multi-FIT networks improve upon the state-of-the-art models for three predictive tasks, including the forecasting of patient survival.


Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome

arXiv.org Artificial Intelligence

In current clinical practice, score-based mortality prediction systems, such as the series of the acute Predicting the risk of mortality for patients with acute physiology and chronic health evaluation (APACHE) scoring myocardial infarction (AMI) using electronic health records system, are widely used to help determine the treatment or (EHRs) data can help identify risky patients who might need medicine should be given to patients admitted into intensive more tailored care. In our previous work, we built care units (ICUs) [10]. Nevertheless, these scoring systems computational models to predict one-year mortality of patients have significant limitations, e.g., 1) they are often restricted to admitted to an intensive care unit (ICU) with AMI or post only few predictors; 2) they have poor generalizability and may myocardial infarction syndrome. Our prior work only used the be less precise when applied to specific subpopulations other structured clinical data from MIMIC-III, a publicly available than the original population used for the initial development; ICU clinical database. In this study, we enhanced our work by and 3) they need to be periodically recalibrated to reflect adding the word embedding features from free-text discharge changes in clinical practice and patient demographics [6].


I-vector Based Features Embedding for Heart Sound Classification

arXiv.org Machine Learning

Cardiovascular disease (CVD) is considered as one of the main causes of death in the world. Accordingly, scientists look for methods to recognize normal/abnormal heart patterns. Over recent years, researchers have been interested in to investigate CVDs based on heart sounds. The physionet 2016 corpus is presented to provide a standard database for researchers in this field. In this study we proposed an approach for normal/abnormal heart sound detection, based on i-vector features on phiysionet 2016 corpus. In this method, a fixed length vector, namely i-vector, is extracted from each record, and then Principal Component Analysis (PCA) is applied. Then Variational AuotoEncoders (VAE) is used to reduce dimensions of the obtained i-vector. After that, this i-vector and its transmitted version by PCA and VAE are used for training two Gaussian Mixture Models (GMMs). Finally, test set is scored using these trained GMMs. In the next step we applied a simple global threshold to classify the obtained scores. We reported the results based on Equal Error Rate (EER) and Modified Accuracy (MAcc). Experimental results show the obtained Accuracy by our proposed system could improve the results reported on the baseline system by 16%.