We determine your cholesterol ratio by dividing your total cholesterol by your HDL number. For instance, if your total cholesterol is 180 and your HDL is 82, your cholesterol ratio is 2.2. According to the American Heart Association (AHA), you should aim to keep your ratio below 5, with the ideal cholesterol ratio being 3.5. Men According to the Framingham Heart Study, a cholesterol ratio of 5 indicates average risk of heart disease for men. Men have double the risk for heart disease if their ratio reaches 9.6, and they have roughly half the average risk for heart disease with a cholesterol ratio of 3.4.
To address the concern for the ethical and societal implications of artificial intelligence systems, a possible solution is to have AI systems be audited for harm by investigators. We at the Frankfurt Big Data Lab at the Goethe University of Frankfurt, together with a team of international experts defined a novel holistic and analytic processes to assess Ethical AI, called Z-Inspection. Z-Inspection is a general inspection process for Ethical AI which can be applied to a variety of domains such as business, healthcare, public sector, etc. To the best of our knowledge, Z-Inspection is the first process that combines a holistic and analytic approach to assess Ethical AI in practice. Our assessment takes into account the "Framework for Trustworthy AI" and the seven key requirements that AI systems should meet in order to be deemed trustworthy, defined by the independent High-Level Expert Group of Artificial Intelligence , set by the European Commission, and also confirmed by a recent report of The Organization for Economic Co-operation and Development (OECD).
Artificial intelligence reduces the routine work burden on physicians. In cardiology, the startup Cardiomatics analyzes data from Holter ECGs and provides physicians with analyses based on algorithms derived from scientific evidence. AF is one of the contexts for this automated evaluation. Long-term ECGs provide AF information relevant for patients who have experienced a cerebrovascular event. Thanks to AI, cardiologists, referrers, as well as patients profit from lean processes and speedy AF diagnosis.
Logistic regression is a popular method since the last century. It establishes the relationship between a categorical variable and one or more independent variables. This relationship is used in machine learning to predict the outcome of a categorical variable. It is widely used in many different fields such as the medical field, trading and business, technology, and many more. This article explains the process of developing a binary classification algorithm and implements it on a medical dataset.
Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients’ survival from their data and can individuate the most important features among those included in their medical records. In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients’ survival. This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.
Brisbane, California-based Caption Health, maker of artificial intelligence-assisted ultrasound guidance software, has tied up a $53 million Series B funding round. Prior backer DCVC headed the raise. Atlantic Bridge and Edwards Lifesciences joined the round as new investors, while Khosla Ventures added onto its previous stake. The company's Caption AI software platform consists of two components, Caption Guidance and Caption Interpretation. The first uses AI to guide medical professionals though cardiac imaging that would normally be conducted by an ultrasound expert.
Machine learning was found to be superior to logistic risk scores in predicting intrahospital all-cause mortality after transcatheter aortic valve implantation (TAVI), according to study results published in Clinical Research in Cardiology. Current strategies for identifying patients eligible for TAVI rely on risk assessment tools such as the Society of Thoracic Surgeon's Risk Score (STS score). The predictive power of these tools is poor, and improved options for risk stratification of TAVI patients are needed. In this retrospective analysis of data from 451 patients, investigators aimed to evaluate whether machine learning models could be used to predict clinical outcomes for patients after TAVI. A total of 83 features, including patient demographics, comorbidities, laboratory data, electro- and echocardiogram findings, and computed tomography (CT) results, were used to train and test the predictive models.
Data Science Fails – If It Looks Too Good To Be True… You've probably seen amazing AI news headlines such as: AI can predict earthquakes. Using just a single heartbeat, an AI achieved 100% accuracy predicting congestive heart failure. AI can diagnose covid19 in seconds from a chest scan. A new marketing model is promising to increase the response rate tenfold. It all seems too good to be true.
Despite significant advances in the diagnosis and management of cardiac disease, cardiovascular disease continues to have high morbidity and mortality. In some cases, the diagnosis is delayed, while in others, the diagnosis is mistaken for another disorder. Advanced technology and machine learning have opened up new opportunities to evaluate image-based data. Currently, image analysis is completely reliant on observer visual assessment and using crude quantitative measures to assess cardiac function and structure. Clinicians agree that there is a need for more advanced analytical techniques that can allow for more refined quantification of imaging phenotypes.
The Australian government has announced it will invest AU$19 million over three years into artificial intelligence-based health research projects designed to prevent, diagnose, and treat a range of health conditions. There are five projects in total that will receive funding as part of this announcement. The Centre for Eye Research Australia and the University of New South Wales (UNSW) will each receive nearly AU$5 million for their research projects. The Centre for Eye Research Australia has developed an AI system to detect eye and cardiovascular diseases, while UNSW is focused on using AI to understand and improve the treatment of mental health, including stress, anxiety, and depression. Another AU$7 million is being put towards two projects developed by the University of Sydney (USyd).