Sepsis early prediction method based on machine learning

Abstract

The invention discloses a sepsis early prediction method based on machine learning. The method comprises the following steps: firstly, extracting clinical data, including demographic statistics (suchas age and gender), vital sign variables (such as heart rate and systolic pressure) and laboratory measurement indexes (such as creatinine and platelet count), of a patient within 24 hours after a patient enters an ICU by utilizing an electronic medical record, preprocessing the data, inputting the preprocessed data into an improved deep forest algorithm model for training, and outputting the disease probability of the patient after training optimization. And meanwhile, the algorithm model can also sort characteristic variables and output an early warning factor which has great influence on early prediction of sepsis. Finally, corresponding variables of the patient needing to be predicted are input into the trained model, so that early prediction of sepsis can be carried out on the patient. According to the invention, early prediction is carried out on sepsis based on a machine learning method, doctors can be assisted in making clinical decisions, and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the field of medical data mining, and in particular relates to a method for early prediction of sepsis based on machine learning. Background technique [0002] Sepsis is a disease that poses a serious threat to life safety. Sepsis is a systemic inflammatory response syndrome caused by infection. It is one of the main causes of common high-risk complications and death in ICU patients. It is estimated that 30 million people in the world suffer from sepsis every year, and more than 6 million people die from sepsis. The cost of sepsis treatment is very high, and the risks are also very high. Due to the high morbidity, mortality and high cost of treatment, sepsis has become a public medical problem of great concern around the world. The clinical diagnosis definition of sepsis has developed from 1.0 to 3.0, and it is also constantly changing and updating. The latest clinical definition of sepsis-3 was proposed by the European Socie...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of difficult and low accuracy in the clinical diagnosis of sepsis in ICU patients in the prior art, the present invention proposes an early prediction method for sepsis based on machine learning, which is based on machine learning and data mining processes Early prediction of sepsis in ICU patients

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