Prediction model for organ failure induced by acute pancreatitis

Abstract

The invention discloses a prediction model for organ failure induced by acute pancreatitis, and the model comprises the following steps: S100, preprocessing patient information, and recording events and time nodes by means of {Variables, Time}; s200, sorting the events according to a time sequence, and filling missing values by adopting a Decay mechanism; and S300, carrying out one-hot coding on the data by using an Embedded mechanism, mapping the data to a real vector space, normalizing the data and then inputting the normalized data into a Phased LSTM model. Time gate output is calculated according to the interval time of a patient from admission to the time node of the event, the model training process is accelerated by utilizing the output result of the time gate, the neuron of the output layer is 2, and a softmax function is adopted as an activation function. According to the method, heterogeneous multi-dimensional data can be processed, time information can be flexibly used, andmeanwhile, the judgment of the model is closer to a description of a disease natural process in the real world.

Description

technical field [0001] The invention relates to the field of neural networks, in particular to a prediction model of organ failure induced by acute pancreatitis. Background technique [0002] The current mathematical models for the prediction of organ failure in acute pancreatitis in my country mainly focus on traditional statistical methods, some of which do not consider the information of the time dimension, and some of which consider the time information but cannot handle heterogeneous events and irregular and non-equidistant sampling sequence data; some algorithms forcibly fill in the data artificially and subjectively, which makes the ability to predict the end point of the event unreliable. The prediction task in the medical field is relatively complex, because there are many factors to be considered. Due to the limitation of its own mechanism, the traditional method cannot comprehensively consider all the data of the patient from the time of admission, which reduces th...

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Problems solved by technology

[0002] The current mathematical models for the prediction of organ failure in acute pancreatitis in my country mainly focus on traditional statistical methods, some of which do not consider the information of the time dimension, and some of which consider the time information but cannot handle heterogeneous events and irregular and non-equidistant sampling sequence data; some algorithms forcibly fill in the data artificially and subjectively, which leads to unreliable prediction ability about the end point of the event

The prediction task in the medical field is relatively complex, because there are many factors to be considered. Due to the limitation of its own mechanism, the traditional method cannot comprehensively consider all the data of the patient from the time of admission, which reduces the utility of the data and is difficult to achieve satisfactory results. The prediction accuracy of

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