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Predictive technologies for identifying cardiac risks

Some medical conditions can be missed until they cause such severe symptoms that they lead to a visit to a North Carolina emergency room. Heart failure is such a disease, often diagnosed in emergency situations. However, electronic health records may be used in the future to predict this and many other conditions through the use of artificial intelligence.

The type of artificial intelligence being used to analyze health records in studies is known as deep learning, which uses graphics processing units to tackle complicated issues. Whereas machine learning is dependent on human involvement to define numerous factors, deep learning moves at a much quicker pace because of the capabilities of GPUs, which do not require the time-consuming input of a human. As data is analyzed rapidly, there is a greater potential for medical intervention before a serious condition can develop.

As researchers move toward providing physicians with access to this type of technology, emergency room errors could be reduced as some conditions become easier to diagnose. The failure to diagnose serious medical conditions could become less common as high-tech approaches to evaluating patient data facilitates a more comprehensive view of an individual's overall health. An individual dealing with serious problems affecting multiple body systems, for example, might not think about the possibility of an autoimmune disease. However, advanced technologies might assimilate electronic health information from each source to identify a common cause.

An individual who has been harmed a missed diagnosis might wonder if there is cause to file a medical malpractice lawsuit against a practitioner. An attorney who has experience with these matters will point out that there has to be a finding that the requisite standard of care was not lived up to.

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Predictive technologies for identifying cardiac risks | Britton Law, P.A.