ICU death rate prediction method and system based on punishment integrated model

An integrated model and prediction method technology, applied in prediction, integrated learning, medical data mining, etc., can solve the limitation of mortality prediction performance, the impact of scoring model is not considered, and the large difference between the number of survivors and deaths of ICU patients after discharge, etc. problem, to achieve the effect of improving classification performance and prediction performance

Active Publication Date: 2020-07-03
HANGZHOU NEURO TECH CO LTD
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Problems solved by technology

These scoring methods can help reduce the pressure on clinicians, but considering that they are all based on linear models, the mortality prediction performance is somewhat limited
In addition, there is a large

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  • ICU death rate prediction method and system based on punishment integrated model
  • ICU death rate prediction method and system based on punishment integrated model
  • ICU death rate prediction method and system based on punishment integrated model

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[0033] figure 1 Shown is a flow chart of a method for predicting ICU mortality based on a penalty ensemble model provided by an embodiment of the present invention. figure 2 shown as figure 1 Schematic diagram of the ICU mortality prediction method for the penalized ensemble model shown. image 3 Shown is a schematic diagram of the principle of training the ensemble model using the 5-fold cross-validation method. Figure 4 Shown is a functional block diagram of the ICU mortality prediction system of the penalized ensemble model provided by an embodiment of the present invention. Please also refer to Figure 1 to Figure 4 .

[0034] like figure 1 and figure 2 As shown, the method for predicting ICU mortality based on the penalty ensemble model provided in this embodiment includes: acquiring multiple original data features of ICU patients from multiple dimensions (step S10 ). Preprocessing is performed on the acquired multiple raw data features (step S20). Mining and e...

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Abstract

The invention provides an ICU death rate prediction method and system based on a punishment integrated model. The ICU death rate prediction method based on the punishment integrated model comprises the following steps: acquiring multiple original data characteristics of an ICU patient from multiple dimensions; preprocessing the plurality of the acquired original data characteristics; mining and extracting new data characteristics on the basis of the original data characteristics; selecting the original data characteristics and the new data characteristics based on an algorithm in the integrated model to form an input characteristic set; and inputting the formed input characteristic set into a trained and tested integrated model to obtain an ICU death rate prediction result, wherein the integrated model integrates a logistic regression algorithm based on a weight punishment strategy and a LightGBM algorithm based on the weight punishment strategy.

Description

technical field [0001] The invention relates to the field of ICU mortality prediction, and in particular to an ICU mortality prediction method and system based on a penalty integrated model. Background technique [0002] The intensive care unit (ICU) concentrates the hospital's most advanced monitoring equipment and first aid facilities, which makes it play an important role in reducing the mortality rate. Predicting the discharge mortality rate of ICU patients helps hospitals rationally allocate medical resources on the one hand, and on the other hand helps clinicians formulate diagnosis and treatment plans, thereby reducing the mortality rate of ICU patients. However, the data formed by ICU equipment is rich and complex, and usually exhibits the characteristics of high dimensionality, imbalance and time asynchrony. Therefore, even experienced clinicians cannot quickly and accurately judge the progression of the disease or the extent to which the disease is affecting the p...

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Application Information

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IPC IPC(8): G16H50/30G16H50/70G06Q10/04G06N20/20
CPCG16H50/30G16H50/70G06Q10/04G06N20/20
Inventor 刘俊飙戴珅懿吴端坡
Owner HANGZHOU NEURO TECH CO LTD
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