3 myths about machine learning in healthcare

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Machine Learning. Most of us have probably heard this term before, but the question is whether we really understand what it is and how it works. There are many explanations for this term, but unless you are deeply immersed in a sea of ​​specialized talk about leaf nodes, split points, and recursion, it can be completely lost.

In short, when it comes to machine learning, all you need to know to get rcs data south africa is that machine learning applies statistical models to the data you have to make intelligent predictions about the data you don't have. Those predictions can help you find signals in the noise and extract value from all the data you collect.

The advantage of using machine learning is its speed. It can mine vast swaths of data in seconds or minutes, finding patterns and making predictions in ways that no human analyst could begin to imitate.

Machine learning has shown its benefits in many industries, and healthcare is one of them. Statistics show that machine learning will dramatically improve healthcare in the next few years.

Below, read about 3 common myths that exist around machine learning in healthcare.

Myth 1: Machine learning can take over much of what doctors do.

The reality is that machine learning applications can perform some of what doctors do today, but they will never replace the bulk of what doctors do in the near future.

Doctors perform three main duties:

They help reduce the rate of illness in people;
They diagnose what things people do wrong;
They provide care and treatment for patients.
Machine learning has made important contributions to the first and second of these functions. For example, machine learning algorithms have proven particularly useful in predicting cancer features from medical images or diagnosing fractures from X-rays. Unsupervised learning algorithms have shown potential for linking disease risks to genomic biomarkers. However, even as these applications develop further, they will not replicate the ability of a physician to provide care and treatment. The output of machine learning must still be analyzed by someone with domain knowledge. Otherwise, trivial data can be interpreted as essential, and essential data as trivial.

There is also a human element in helping patients decide whether and how to receive treatment. Patients often have concerns or fears about undergoing treatment. Physicians should consider the patient’s mental state, expectations, and history in making shared decisions with the patient and their family. Patients value this human interaction, and not getting it at sensitive times can be distressing.

Finally, once treatment is complete, the recovery process itself requires close monitoring and care. Complications are often detected through clinical observation as opposed to protocol-guided testing or diagnostics.

Myth 2: Big data + brilliant data scientists are always a recipe for success.

The reality is that they are necessary, but not sufficient. More data is better, but only if it is the right data and people fully understand it. At this point, it is very useful to ask a few key questions:

How was the data collected?
Consider how the adoption of electronic health records could lead to all diagnoses and medications prescribed by different doctors for a patient being included in a single record that would be more comprehensive than the paper records of individual doctors. Without taking this change into account, it could be wrongly concluded that patients suddenly became ill.