Method and system for predicting mortality of acute respiratory distress syndrome (ARDS)

Abstract

The invention discloses a method and system for predicting mortality of acute respiratory distress syndrome (ARDS). The method comprises obtaining medical data of ARDS patients; adopting a machine learning method to perform model training according to the obtained medical data and obtaining an ARDS mortality prediction model; adopting the ARDS mortality prediction model to predict an object to bepredicted to obtain a prediction result of the mortality of the ARDS. The machine learning method is adopted to train the ARDS mortality prediction model, then the ARDS mortality prediction model is adopted to predict the mortality of the ARDS patients, and the machine learning is applied to the mortality prediction of the ARDS patients. The mortality of The ARDS patients can be accurately and objectively predicted by the model trained by the machine learning to provide clinicians with more effective and feasible prediction information, and the method and system can be widely used in the fields of medical data mining.

Description

technical field [0001] The invention relates to the field of medical data mining, in particular to a method and system for predicting mortality of acute respiratory distress syndrome. Background technique [0002] Acute Respiratory Distress Syndrome (ARDS) is a common critical illness, which refers to acute diffuse lung injury related to exposure to risk factors, often accompanied by increased pulmonary vascular permeability caused by lung inflammation and Air-containing lung tissue is reduced. Clinically, all kinds of critically ill patients have the potential risk of developing ARDS, and the in-hospital mortality rate after the occurrence of ARDS is between 34.9% and 46.1%, which seriously threatens the life of critically ill patients and affects their quality of life. Therefore, the establishment of a mortality prediction model for ARDS patients can distinguish the severity of the disease and determine different treatment strategies; at the same time, the use of the pred...

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Problems solved by technology

However, this type of method has the following problems: (1) The variables obtained from expert experience or statistical analysis will have subjectivity and data bias; (2) The factors affecting the occurrence and development of ARDS are extremely complex, and it is difficult to combine multidimensional variables for statistical analysis (3) These methods are not specially designed for ARDS, and there is no effective scoring model suitable for predicting the mortality of ARDS patients

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