Acute kidney injury patient death rate prediction method, server and storage medium

Abstract

The embodiment of the invention discloses an acute kidney injury patient death rate prediction method and device, a server and a storage medium. The method comprises the steps of generating medical feature data of a specific structure according to clinical medical data of a to-be-tested patient; and selecting a corresponding pre-trained random forest model according to the survival state and survival days of the to-be-tested patient, inputting the medical feature data of the to-be-tested patient into the corresponding pre-trained random forest model, and outputting the death rate of the to-be-tested patient. According to the technical scheme of the embodiment of the invention, the death rate of the acute kidney injury patient is predicted through the random forest model, the structured clinical data is used for training, and the logistic regression algorithm is used to calibrate the model, so that fuzzy clinical definition and data acquisition deviation are effectively avoided, and theaccuracy of predicting the death rate of the acute kidney injury patient is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of machine learning, and in particular to a method, device, server, and storage medium for predicting mortality of patients with acute kidney injury. Background technique [0002] Acute kidney injury is associated with high morbidity and mortality in hospitalized patients. Risk stratification of patients with acute kidney injury on admission is important to better allocate medical resources and provide precise individualized care. However, improving mortality prediction in hospitalized patients remains an important challenge. [0003] Traditional methods for predicting the survival rate of admitted patients are based on traditional analysis methods, including SOFA, SAPSII, Elixhauser_sid30, etc. Traditional methods usually collect data in one or more medical centers, and then obtain relevant variables based on the experience of disease experts and statistical methods (the most com...

AI Technical Summary

Problems solved by technology

However, this type of method has the following problems: ①The variables obtained from expert experience or statistical analysis will have subjectivity and data deviation; ②The factors affecting the occurrence and development of acute kidney injury are extremely complex, and it is difficult to combine multi-dimensional variables for statistical analysis; ③ These methods were not specifically designed for acute kidney injury and were not designed to predict mortality, and no valid scoring model exists to predict mortality in patients with acute kidney injury

Thus although a number of severity scores have been proposed, validation studies concluded that they do not accurately predict acute kidney injury mortality

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