Death prediction method and system for sepsis-related acute kidney injury patient, equipment and medium

A technology of acute kidney injury and prediction method, which is applied in the field of healthcare informatics, can solve the problems of poor prediction efficiency and prediction accuracy, and achieve the effect of improving prediction efficiency and accuracy

Pending Publication Date: 2022-02-11
XIANGYA HOSPITAL CENT SOUTH UNIV
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[0004] In view of this, embodiments of the present disclosure provide a death prediction method, system, device and medium for patients with se

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  • Death prediction method and system for sepsis-related acute kidney injury patient, equipment and medium
  • Death prediction method and system for sepsis-related acute kidney injury patient, equipment and medium
  • Death prediction method and system for sepsis-related acute kidney injury patient, equipment and medium

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[0037] Embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings.

[0038] Embodiments of the present disclosure are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Apparently, the described embodiments are only some of the embodiments of the present disclosure, not all of them. The present disclosure can also be implemented or applied through different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the case of no conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, a...

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Abstract

The embodiment of the invention provides a death prediction method and system for a sepsis-related acute kidney injury patient, equipment and a medium, and belongs to the technical field of medical care information. The method specifically comprises the steps of training an XGBOOST algorithm through a sample data set, and establishing a prediction model; collecting the urine volume, the maximum value of urea nitrogen, the noradrenaline injection rate, the maximum value of anion gap, the maximum value of creatinine, the maximum value of red blood cell distribution width, the minimum value of international standardization ratio, the maximum value of heart rate, the maximum value of body temperature, the minimum value of oxygen uptake fraction, the stroke state, the minimum value of creatinine, the minimum Glasgow coma scale and a diabetes state of a target person; and inputting the key information data set into a prediction model to obtain a prediction result. According to the scheme, the machine learning algorithm is used for learning, the prediction model is established, then the collected key information data set is input into the prediction model, the prediction result is obtained, and the prediction efficiency and accuracy are improved.

Description

technical field [0001] Embodiments of the present disclosure relate to the technical field of healthcare informatics, and in particular to a method, system, device and medium for predicting death of patients with sepsis-associated acute kidney injury. Background technique [0002] At present, sepsis is a common disease and has become an important public health problem worldwide, causing 5.3 million deaths every year, with an overall mortality rate of about 30%, and a higher mortality rate in the intensive care unit (ICU). Sepsis-associated acute kidney injury (S-AKI) is a common complication in critically ill patients, often associated with higher morbidity and mortality, and its severity is directly proportional to the risk of death. A retrospective study (including 146,148 patients) conducted in China found that AKI was present in 47.1% of sepsis patients. Therefore, early prediction of patients' risk of death is crucial in providing clinicians with practical clinical dec...

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Application Information

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IPC IPC(8): G16H50/70G06Q10/04G06N3/08G06K9/62G06F30/27
CPCG16H50/70G06F30/27G06Q10/04G06N3/08G06F18/214
Inventor 袁琼靖刘乐平周泓杉
Owner XIANGYA HOSPITAL CENT SOUTH UNIV
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