This database contains 14 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "target" field refers to the presence of heart disease in the patient. It is integer-valued from 0 and 1. To get the information about the data set.
A new model that uses machine learning, which is a type of artificial intelligence, may help predict which patients with kidney disease are at especially high risk of developing heartbeat irregularities. Atrial fibrillation (AF)--an irregular, often rapid heart rate--is common in patients with chronic kidney disease (CKD) and is associated with poor kidney and cardiovascular outcomes. For the results, researchers at the University of Washington in the US conducted a study to see if a new prediction model could be used to identify patients with chronic kidney disease at the highest risk of experiencing atrial fibrillation. The team compared a previously published atrial fibrillation prediction model with a model developed using machine learning (a type of artificial intelligence) based on clinical variables and cardiac markers. In an analysis of information on 2,766 participants in the Chronic Renal Insufficiency Cohort (CRIC), the model based on machine learning was superior to the previously published model for predicting atrial fibrillation.
The evolution of artificial intelligence and related technologies have the potential to drastically increase the clinical importance of automated diagnosis tools. Putting these tools into use, however, is challenging, since the algorithm outcome will be used to make clinical decisions and wrong predictions can prevent the most appropriate treatment from being provided to the patient. Models should not only provide accurate predictions, but also evidence that supports the outcomes, so they can be audited, and their predictions double-checked. Some models are constructed in such a way they are difficult to interpret, hence the name black-box models. While there are methods that generate explanations for generic black-box classifiers,9 the solutions are usually not tailored for the needs of physicians and do not take any medical background into consideration.
An international team of researchers has developed a way to use artificial intelligence to predict the risk of a patient developing cardiovascular disease. In their paper published in the journal Nature Biological Engineering, the group describes using retinal blood vessel scans as a data-source for a deep learning system to teach it to recognize the signs of cardiovascular disease in people. For over 100 years, doctors have peered into the eyes of patients looking for changes in retinal vasculature--blood vessels in the retina that can reflect the impact of high blood pressure over a period of time. Such an impact can be an indicator of impending cardiovascular disease. Over time, medical scientists have developed instruments that allow eye doctors to get a better look at the parts of the eye most susceptible to damage from hypertension and have used them as a part of a process to diagnose patients that are likely to develop the disease.
According to Healthline, Pulmonary edema is a condition in which the lungs fill with fluid. It's also known as lung congestion, lung water, and pulmonary congestion. When pulmonary edema occurs, the body struggles to get enough oxygen and you start to have shortness of breath. But timely treatment for pulmonary edema and its underlying cause can improve possible outcomes. The most common cause of pulmonary edema is congestive heart failure (CHF).
Datasets often have missing values and this can cause problems for machine learning algorithms. It is considered good practise to identify and replace missing values in each column of your dateset prior to performing predictive modelling. This method of missing data replacement is referred to as data imputation. Missing values in a dataset can arise due to a multitude of reasons. These commonly include, but are not limited to; malfunctioning measuring equipment, collation of non-identical datasets and changes in data collection during an experiment.
We are witnessing a rapid advance of artificial intelligence (AI) developments in different fields such as medicine. Researchers from different disciplines, including cardiac modelers, are aware of the advantages of combining machine learning and deep learning techniques with classical modeling tools to improve image segmentation outcomes, model parameter estimation, perform data-driven reduction models, or predict the outcome of complex cardiac therapies.Modeling and simulation of cardiac function is a very challenging area that can benefit from modern AI technologies, which will enable its translation into clinical environments by improving the accuracy or reducing the cost of biophysical computer simulations.In this Research Topic we would like to explore the potential benefits of combining AI with traditional physics-based mechanistic modeling techniques employed by cardiac modelers. Cardiac modeling and simulation has become increasingly complex with challenges ranging from the need to integrate experimental and clinical imaging and recording data into the model, to properly address and understand the uncertainty within these models, and to employ them in clinical workflows for fast calibration and prediction. The goal of this Research Topic is to sample and showcase the collective efforts of using AI to address these emerging challenges, covering potential topics from the construction of the computational model that will involve the segmentation of the heart and grea...
Artificial intelligence in cardiology but before I do that let me just give you a brief introduction of artificial intelligence in general so what is artificial intelligence artificial intelligence is defined as the ability to make computers or machines learn to solve problems that will otherwise require a human to do it now we hear about AI every day but more importantly we are using artificial intelligence or AI as we call every day we use it with our cell phones especially if you have face recognition fingerprint recognition every time you do a Google search the computer already knows your preferences your taste is your likes and will accommodate those searches according to your personal history that's something the computer has been learning. The banks are using AI to monitor transactions to detect fraud so AI is being used everywhere every day and we are using that for morning tonight there are few things that are important to clarify when we talk about AI or artificial ...
An international team of researchers has developed a way to use artificial intelligence to predict the risk of a patient developing cardiovascular disease. In their paper published in the journal Nature Biological Engineering, the group describes using retinal blood vessel scans as a data-source for a deep learning system to teach it to recognize the signs of cardiovascular disease in people. For over 100 years, doctors have peered into the eyes of patients looking for changes in retinal vasculature--blood vessels in the retina that can reflect the impact of high blood pressure over a period of time. Such an impact can be an indicator of impending cardiovascular disease. Over time, medical scientists have developed instruments that allow eye doctors to get a better look at the parts of the eye most susceptible to damage from hypertension and have used them as a part of a process to diagnose patients that are likely to develop the disease. But such tools still require a medical professional to make the final call.
To elucidate the genetics of coronary artery disease (CAD) in the Japanese population, researchers conducted a large-scale genome-wide association study of 168,228 individuals of Japanese ancestry (25,892 cases and 142,336 controls) with genotype imputation using a newly developed reference panel of Japanese haplotypes including 1,781 CAD cases and 2,636 controls. They detected eight new susceptibility loci and Japanese-specific rare variants contributing to disease severity and increased cardiovascular mortality. They then conducted a trans-ancestry meta-analysis and discovered 35 additional new loci. Using the meta-analysis results, they derived a polygenic risk score (PRS) for CAD, which outperformed those derived from either Japanese or European genome-wide association studies. Researchers manually curated a set of 255 splice events detected in a large-scale tissue-based proteomics experiment and found that more than a third had evidence of significant tissue-specific differences.