Machine Learning in Healthcare

  • 6 months ago
  • 8 min read

Machine learning has deeply integrated into our lives, and one cannot imagine the modern world without this technology: your smartphone recognizes your face, or your Internet browser picks up the most suitable links based on your internet history. And the use of the technology in many industries is only increasing.

First of all, it is vital to understand what machine learning is. Most computer programs cannot learn on their own, they only perform a set of predetermined operations. This is an extensive subsection of artificial intelligence that studies methods for constructing algorithms capable of learning. Machine learning is at the junction of mathematical statistics, optimization methods and classical mathematics, but it also has its own specifics.

Healthcare is one of the industries that enjoy the benefits of machine learning. Algorithms do not only identify diseases at an early stage, but also determine the treatment outcomes, gather anamneses, and perform other complex medical tasks. Let’s dig deep into the topic and find out how machine learning is applied in healthcare software development and how it helps to improve diagnostics, treatment, and overall patient care.

 

Patient data analysis

Every medical image, examination protocol and anamnesis contains information that allow making an accurate diagnosis and prescribe the appropriate treatment. Unfortunately, even experienced doctors cannot always see full disease patterns because the data in medical records may not be structured well, and the medical history may be too voluminous. Stressful working conditions and fatigue also affect doctors’ performance, not mentioning the fact that doctors may lack knowledge in some narrow areas.

Some diseases, such as cancer, can be defeated successfully if non-obvious symptoms and signs are recognized in time. So, machine learning algorithms analyze all the symptoms, compare the results with hundreds of similar cases and aid in providing the necessary and timely medical treatment. This technology also helps to prevent relapses by assisting in subsequent cases providing additional recommendations.

 

Diagnostics

One of the most significant advantages of machine learning is the ability to find serious diseases before the first symptoms appear, for example, when people undergo regular medical examinations. Here are several examples of how machine learning is applied in diagnostics.

Chronic diseases. Machine learning enables hospitals to identify patients with an incorrect diagnosis or undiagnosed chronic diseases and also predict the possibility of the risk of
chronic conditions in the future. Timely identification of a disease can help reduce the number of chronic patients.

Cardio diseases diagnostics. Millions of people worldwide suffer from high blood pressure, cardiovascular and other heart-related diseases. According to last year’s report, 1 out of 20 deaths happens because of a heart stroke. Machine learning and artificial intelligence help reduce the mortality rate due to heart diseases by offering timely diagnosis.

Cancer diagnostics. Since cancer is one of the most serious illnesses nowadays, attention is paid to research into this disease. Machine learning assists in detecting cancer cells by analyzing images of tissues and helps to diagnose cancer before the first visible symptoms emerge, for instance, during regular health examinations.

 

Clinical trials

The process of clinical trials is considered to be the most sophisticated part of drug development. All the data collected from the participants should be processed and accurately analyzed. Machine learning algorithms help predict the results of clinical trials and minimize possible risks.

 

Conclusion

Using more sensitive algorithms and modern computing power, machine learning systems can significantly improve the work of medical staff. Algorithms are used everywhere where there is a large flow of complex data. The use of machine learning can solve a wide range of complex tasks in all areas of medicine. The development of data storage systems, availability of computers, and easy access to the necessary mathematical algorithms make this area one of the most dynamically developing with a wide-open field for everyone, including industry newcomers, to contribute to the common endeavor.

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