A new machine learning artificial intelligence (AI) model has been introduced in Russia, informs Naked Science. Its main objective is to accurately predict the risk of developing heart failure even before its first symptoms appear.

The human learning model was trained on large amounts of anonymized and publicly available medical data. These data include laboratory test results, clinical research, and information from the patient's medical history and lifestyle. This integrated approach enables the algorithm to find complex patterns.

According to the author of this development, Timur Bitsuev, a staff member of the Laboratory of Intelligent Systems and Data Analysis, this system opens up new prospects for cardiologists. Its main advantage is its ability to identify the risk of heart failure even before the appearance of obvious clinical symptoms. And this enables doctors to pre-select the optimal tactics for monitoring and managing the patient.

“The system does not replace the doctor, but acts as a smart assistant, providing a data-based, objective assessment. This will help the specialist make more informed decisions about additional examinations, prescribing therapy, or lifestyle changes. Machine learning algorithms can find hidden connections in the data that may remain unnoticed by traditional analysis, thus indicating previously underestimated risk factors,” says Bitsuev.

Then, this will help medical institutions reduce the number of hospitalizations and the time required to serve each patient.

In addition to contributing to early diagnosis and an individualized approach to the patient, this development promises systemic advantages: reduction of the number of hospitalizations through early detection and risk management, and reduction of the time required to serve each patient, as the doctor receives structured analytical support for decision-making.