Predicting patient death
About project
The model made it possible to predict the likelihood of death of patients and separate severe cases from the lungs.
Development of a model that predicts the likelihood of patient death. For analysis, data from biological analyzes of the patient and various socio-demographic indicators are received.
A task
It is necessary to predict the mortality of patients in a medical clinic within a given period of time and in general.
Stages of development
XGBoost acts as a machine learning algorithm. The model prediction add-on, xgbse, was also used.
Model implementation
Building a model
Creating a Showcase
External data collection
Project team
Frank S.
Areas of use
This technology has applications in medical clinics. Based on the results of the model, doctors are given a recommendation system for predicting the death of a patient.
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