Applications Of Machine Learning In Healthcare

Machine learning (ML) is an area of ​​artificial intelligence that allows computers to learn automatically. This learning is acquired by processing a large amount of data, which the system uses to gain experience and constant improvement.

The goal is to use results from past experiences to make better decisions in the future. Machine learning in healthcare takes on those tasks that take time and increase the workload of professionals. Patients also benefit, as they can count on more accurate diagnoses and treatments.

Do you want to know more uses of machine learning for health? Could you keep reading to check it out?

Support In Diagnostics

Healthcare professionals face a problem getting an accurate diagnosis from a large volume of data. Machine learning-based tools are essential in this regard, as they can process this data automatically in a matter of seconds, giving the doctor more time to focus on other tasks.

Programs are presented with a vast amount of digitized data, such as MRIs, CT scans, and electrocardiograms, so they can learn to interpret it. In this way, their algorithms can identify patterns in this information that point to disease.

You can also use data from different sources to predict disease and track patient progress. For example, patient history, exams, periodic assessments, and real-time vital signs are used to identify risks for heart attack, stroke, and other complications.

Google Tool To Detect Breast Cancer

Google introduced its newest breast cancer detection tool to the world, capable of recognizing cellular patterns from tissue samples. The idea proposes to optimize the location of tumors that have spread to the lymph nodes close to the breast.

Personalized Treatments

Each patient responds to treatment differently. Therefore, each case that arrives at the clinic or hospital needs to be evaluated separately so that the doctor can develop a treatment that meets the specific needs of each one.

However, this is a tiring and error-prone task, as numerous factors must be considered. This all impacts the quality of life of the patient, who still needs to get used to the treatments.

With machine learning, it is possible to automate this process, as its algorithms can predict how the patient will respond to a specific treatment. The system uses previous patients to reference their treatments with the results obtained. The clinician can then use the result of this comparison to customize their approach.

Automation Of Administrative Tasks

Technology has made administrative work much more accessible, especially with the electronic recording of information. However, most of these tasks still need to be performed manually by health professionals, who feel overwhelmed having to record, review and update so much information constantly.

These activities can very well be attributed to a machine learning-based system, which can perform them much faster and more efficiently. In addition to saving physicians time, this intelligence facilitates access to patient clinical data, improves workflow, and makes information more accurate.

Drug Development

Finding substances with healing potential and developing drugs from them are time-consuming and resource-intensive processes, such as financial investment and years of hard work for researchers.

The amount of data generated in each survey is immense, and even the most used software today cannot process it so efficiently. With machine learning, this is no longer an obstacle, as it is possible to generate fast and accurate results from data collected over decades of studies.

Machine learning algorithms can find the biological origin of the disease and possible targets for its treatment, as well as compounds that interact with that target in the desired way. They also facilitate the choice of ideal candidates for clinical trials and identify the presence of biomarkers, which confirm whether the person has the disease and at what level of severity.

Disease Prediction

Machine learning-based technologies are already being used to predict disease outbreaks worldwide. With the help of artificial neural networks, scientists can group data collected from satellites, social networks, web pages, and many other sources to know in which regions malaria, dengue, zika, and serious infectious diseases, for example, can occur.

Knowing the dynamics of serious diseases is extremely important for organizations that deal with the population’s health since this allows them to adopt preventive measures to avoid contagion and transmission. In the case of people affected by the disease, it is possible to detect it in the early stages and start treatment before the condition worsens.

Such procedures would result in significant savings for public coffers since investments in prevention eliminate part of the expenses with expensive treatments when they exist. Another positive aspect is the increase in the population’s quality of life, especially in deprived areas that are usually more exposed to infectious diseases.

Google’s Healthcare Solution

One of the significant problems in the health area is the difficulty of accessing patient data, which arises from different sources and in different modalities. Organizations often have access to only a portion of medical records, compromising treatments due to the lack of accurate information.

With that in mind, Google created the Healthcare API, a Google Cloud application that uses machine learning analytics products to intelligently unify healthcare data across organizations. The solution is entirely secure, as it contains tools that give the organization complete control over its data and guarantees against loss, encryption, and many other utilities.

Machine learning in the health area brings numerous innovations, which are in no way intended to replace the work of doctors but rather to facilitate their work. Machines can only interpret the information they were taught. The knowledge and experience of these professionals continue to be essential for advances in the population’s well-being.

Also Read: 6 Tips To Start Learning About Machine Learning

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