heart attack


New method could help doctors avoid ineffective or unnecessarily risky treatments

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In a usual management setting, after a person has had a heart attack or stroke, algorithmic risk models are used to calculate the risk of death for the patient. These algorithms or models utilize various factors such as age of the patient, gender, previous history, family history, ethnicity etc. Treatment of the patient is often guided by these models. A new study has shown that in many cases these models fail to predict the risks accurately. This may lead to treatment choices that are unnecessary or ineffective and even risky for the patients. The new study was published in the Digital Medicine.


AI's future is entirely human-centric

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One of the most straightforward ways to think about artificial intelligence is to define it as a machine's ability to understand or perform in a way that would normally need human intelligence. A lot of what we see called AI in software and cloud services is closer to automation. Automation is based on input and response: an input of X equals a response of Y. With the growing complexity of algorithms, the calculations undertaken to decide how to respond to a given input can be extremely complicated and so automated responses created in this way can give the impression of intelligence. But in reality they are predetermined.


Data Science with Machine Learning Course Training Part 2

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But doing more with that data using machine learning is just what retailers need to really succeed in the current market. Machine Learning in Retail 5. 5 03 Data Science Applications …4 Skin Cancer Deduction Facial Recognition HR Analytics Recommendations 6. 6 04 Visual Analytics with Tableau 7. 7 05 Business Statistics Various Data Types a)discrete, b)continuous, c)Nominal, d) Ordinal, e) Interval Scale, f) Ratio • Central Tendency, • Measures of Dispersion • Random Variable • Probability Distribution • Normal Distribution • Skewness • Kurtosis • Random Sample • Confidence Interval • Sampling Frame • Z-Calculations • Central Limit Theorem • Chi-Square Test Analysis on X and Y data: One-Way Anova, 2 Sample t-test, Case Study on One-Way Anova and 2 Sample T Test,Hypothesis Testing Converting Statistics.. Hmm! 8. 8 06 Machine Learning Enabling machine to learn without being explicitly programmed 9. 9 DataMites is a global institute of Data Science, Machine Learning, IoT and Artificial Intelligence Training and Consulting for individuals and Corporate. For courses enquires Call: 1 415 8522477 (USA) 1800 200 6848 (India Toll Free) Email: enquiry@datamites.com But doing more with that data using machine learning is just what retailers need to really succeed in the current market. For courses enquires Call: 1 415 8522477 (USA) 1800 200 6848 (India Toll Free) Email: enquiry@datamites.com


Machine learning could predict death or heart attack with over 90% accuracy: Study

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Washington DC: A study claimed that machine learning, modern bedrock of artificial intelligence, could predict death or heart attack with more than 90 per cent accuracy. The study was presented at The International Conference on Nuclear Cardiology and Cardiac CT (ICNC) 2019. Machine learning is used every day. Google's search engine, face recognition on smartphones, self-driving cars, Netflix and Spotify recommendation systems -- all use machine learning algorithms to adapt to the individual user. By repeatedly analysing 85 variables in 950 patients with known six-year outcomes, an algorithm'learned' how imaging data interacts.


Artificial Intelligence Identifies Patients with Potentially Fatal Genetic Disease

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A Stanford University-led team of scientists has developed a machine learning tool that can analyse electronic healthcare records (EHR) to identify individuals who are likely to have familial hypercholesterolemia (FH), an underdiagnosed genetic cause of elevated low-density lipoprotein (LDL) cholesterol, which puts patients at a 20-fold increased risk of coronary artery disease. In separate test runs the classifier, described today in npj Digital Medicine, correctly identified more than 80% of cases--its positive predictive value (PPV)--and demonstrated 99% specificity. The team says the classifier could help to flag up patients who are most likely to have FH, so that they and their families can undergo further genetic testing. "Theoretically, when someone comes into the clinic with high cholesterol or heart disease, we would run this algorithm," said Nigam Shah, MBBS, PhD, Stanford University associate professor of medicine and biomedical data science. "If they're flagged, it means there's an 80% chance that they have FH. Those few individuals could then get sequenced to confirm the diagnosis and could start an LDL-lowering treatment right away."


AI tech founder urges business leaders, innovators to consider ethical responsibility

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It is incumbent upon business leaders and Australian organisations to put diversity and the ethical implications of artificial intelligence (AI) at the heart of innovation if we're to ensure the world's third major disruptive force is harnessed for human good. That was the big call-out made by Dr Catriona Wallace, founder and executive director of the ASX-listed machine learning tech innovator, Flamingo AI, during this week's CeBIT conference in Sydney. Speaking on the rise of AI and the relationship between humans and machines, the entrepreneur highlighted several facts and figures on the extent of AI impact and innovation over the short and longer-term horizon, as well as the good and negative potential human consequences that come with it. As outlined by Dr Wallace, disruptive technologies, such as AI, are predicted to be the third of three major problems the world is facing that could detrimentally affect humanity. The other two are climate change, and nuclear war.


Machine learning screens patients for life-threatening genetic disease

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Using large healthcare encounter datasets, a machine learning algorithm is able to identify patients with a common genetic disorder that carries a high risk for early heart attacks and strokes. While individuals with familial hypercholesterolaemia (FH) have 20 times the risk of developing cardiovascular disease than the general population, fewer than 10 percent of the 1.3 million Americans born with the genetic disease are diagnosed. "People born with familial hypercholesterolemia develop cardiovascular damage by puberty, often culminating in early heart attacks or the need for surgery as young or middle-aged adults," says Katherine Wilemon, founder and CEO of the FH Foundation, a non-profit research and advocacy organization. "Since diagnosis of this deadly but treatable condition has stalled in the American medical system, the FH Foundation harnessed artificial intelligence and big data to accelerate identification of those most likely to have FH." In a new study, a machine learning model created by the FH Foundation successfully leveraged healthcare encounter databases to identify individuals with the genetic disorder.


Abbott Announces New Data That Shows Artificial Intelligence Technology Can Help Doctors Better Determine Which Patients are Having a Heart Attack - Sep 10, 2019

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Abbott (NYSE: ABT) announced today that new research, published in the journal Circulation, found its algorithm could help doctors in hospital emergency rooms more accurately determine if someone is having a heart attack or not, so that they can receive faster treatments or be safely discharged.1 In this study, researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand looked at more than 11,000 patients to determine if Abbott's technology developed using artificial intelligence (AI) could provide a faster, more accurate determination that someone is having a heart attack or not. The study found that the algorithm provided doctors a more comprehensive analysis of the probability that a patient was having a heart attack or not, particularly for those who entered the hospital within the first three hours of when their symptoms started. "With machine learning technology, you can go from a one-size-fits-all approach for diagnosing heart attacks to an individualized and more precise risk assessment that looks at how all the variables interact at that moment in time," said Fred Apple, Ph.D., Hennepin HealthCare/ Hennepin County Medical Center, professor of Laboratory Medicine and Pathology at the University of Minnesota, and one of the study authors. "This could give doctors in the ER more personalized, timely and accurate information to determine if their patient is having a heart attack or not." A team of physicians and statisticians at Abbott developed the algorithm* using AI tools to analyze extensive data sets and identify the variables most predictive for determining a cardiac event, such as age, sex and a person's specific troponin levels (using a high sensitivity troponin-I blood test**) and blood sample timing.


Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer

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We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range 0.58–0.71 Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3–8 times higher risk than typical individuals.