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Cardiology Homepage

Scientists create first 3-D image of the heart’s cardiac conduction system Could help doctors treat cardiac arrhythmia and other conditions

RTI Surgical sells its cardiothoracic closure business to A&E Medical for $60 million Proceeds of sale will go toward paying off term loan

Trial study finds cost-saving alternative for Atrial Fibrillation Cryoballoon ablation can reduce rehospitalizations

Philips relaunches Pioneer Plus catheter with intravascular ultrasound guidance The company had its full market release of the product in May

Researchers develop new PET/CT technique to more accurately diagnose cardiac sarcoidosis Can spot the disease in other organs as well

GE Healthcare unveils Productivity Elevated release of cardiac ultrasound system A 'major upgrade' of its cSound diagnostic suite

German researchers develop new PET tracer for detecting blood clots Could help inform smarter use of anticoagulants

GE Healthcare and HeartFlow collaborate against coronary artery disease Combining GE CT systems with HeartFlow's FFRct Analysis system

Handheld scanner for diagnosing heart disease under development in EU Could drastically reduce the cost for standard approaches

Researchers develop new method for 3-D printing custom heart valve models May help plan surgery and avoid complications

Made possible with the latest
advances in artificial intelligence

IBM and Sutter Health partner to predict heart failure risk using EHR data

by Lauren Dubinsky , Senior Reporter
After three years of research, a team of scientists from IBM Research and Sutter Health have developed methods that can predict heart failure using data from patients' EHRs.

The team used the most recent advancements in artificial intelligence, including natural language processing, machine learning, and big data analytics to train models to predict patients at risk.

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Physicians typically use CT angiography and stress tests with SPECT myocardial imaging to predict which patients are likely to suffer a heart attack or other adverse cardiovascular event. They also document signs and symptoms of heart failure in the patients' record.

However, patients are usually diagnosed with heart failure after an acute event, according to the research team. By that time, the patient is already hospitalized with advanced-stage disease and potentially irreversible and progressive organ damage.

While researching, the team found crucial insight into the practical trade-offs and types of data that are required to train models. They also developed potential new application methods that could attract more physicians to adopt future models.

For example, they found that only six of the 28 original risk factors contained within the Framingham Heart Failure Signs and Symptoms (FHFSS) were consistent predictors of a future heart failure diagnosis.

They also found that combining other data routinely collected in EHRs, including disease diagnoses, medication prescriptions and lab tests with FHFSS, could be helpful predictors of a patient's risk of heart failure.

The IBM Research and Sutter Health scientists will continue to work together to improve the accuracy and clinical relevance of the models and to test them for clinical use.

They noted that the availability of big data and advancements in cognitive computing could result in "dramatic advances in earlier disease detection." Based on a MarketsandMarkets report from earlier this month, the use of big data is set to grow in coming years.

The report found that the global artificial intelligence in health care market will grow from $667 million in 2016 to $8 billion by 2022, with big data being one of the main drivers.

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