Survivors data library for accurate risk prediction in medicine and industry developed at Moscow State University

Specialists from the Faculty of Computational Mathematics and Cybernetics at Moscow State University named after M. V. Lomonosov have created the Survivors data library, which helps to assess the risk of patient death and the likelihood of medical equipment failure.

The Survivors library is developed in Python and is designed for analyzing events over time. It takes into account complex dependencies in the data and works correctly even with missing values, which increases the accuracy of predictions.

The versatility of the tool is noted. In medicine, it is used to predict risks and assess the life expectancy of patients based on rules understandable to experts. In CRM systems, Survivors calculates the likelihood of customer churn, and in industry, it predicts equipment degradation and monitors the condition of systems.

Traditional survival analysis methods require strict assumptions about the distribution of events and complex data preparation. The new library uses machine learning and integrates into existing analytical systems, avoiding these limitations.

Survivors does not require data preprocessing and provides high prediction accuracy. The developers sought to create a tool capable of working with real and incomplete data, taking into account their features and uncertainty.

The library's algorithms are optimized for processing large datasets using parallel computing and efficient partitioning methods. Testing on nine open medical and industrial datasets showed stable and accurate predictions, surpassing classical models with minimal settings.

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