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Method for predicting heparin dosage in continuous renal replacement therapy process

A treatment process and prediction method technology, applied in the field of machine learning, can solve problems such as high experience requirements and risks, and achieve the effect of improving the quality of surgery

Active Publication Date: 2018-11-16
DALIAN UNIV OF TECH
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Problems solved by technology

At the same time, in the face of a large amount of clinical laboratory data in reality, the first dose is often given according to the doctor's experience. This method requires a high degree of doctor's experience and has certain risks.

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  • Method for predicting heparin dosage in continuous renal replacement therapy process
  • Method for predicting heparin dosage in continuous renal replacement therapy process
  • Method for predicting heparin dosage in continuous renal replacement therapy process

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with accompanying drawing.

[0031] Such as figure 1 As shown, a method for predicting heparin dose during continuous renal replacement therapy includes the following steps:

[0032]Step 1. Install related software and libraries, install Sublime Text 3 software and an open source Python 3.5, and install scientific computing basic library namely numpy, data analysis library namely pandas, 2D drawing library namely matplotlib and missing data visualization library namely missingno, specifically The installation steps are: under the windows 10 system, first install Python 3.5, press win+R to quickly start the command window, enter cmd, click OK, enter pip install+ required libraries, press enter to install the relevant libraries, pip install matplotlib, press enter to install the matplotlib library, and then download the Sublime Text3 software;

[0033] Step 2. Analyze and understand the relevant inform...

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Abstract

The invention relates to the technical field of machine learning and to a method for predicting a heparin dosage in a continuous renal replacement therapy (CRPT) process, comprising the following steps: (1) installing relevant software and libraries; (2) analyzing and understanding the relevant information of a data set collected by a hospital; (3) preprocessing data; (4) extracting features, (5)determining a training set and a test set and processing unbalanced data; (6) predicting and evaluating a model; and (7) processing the abnormal values of predicted values. The method predicts the heparin dosage in the CRRT process by using a machine learning algorithm such as a gradient boosting regression model, and can give a reasonable and reliable heparin dosage reference value in the CRRT process by further processing the predicted value according to the trained model, thereby assisting the doctors, improving surgery quality, and achieving good economical efficiency and reliability.

Description

technical field [0001] The invention relates to a method for predicting heparin dose during continuous renal replacement therapy, and belongs to the technical field of machine learning. Background technique [0002] At this stage, there are relatively few studies on anticoagulant dosage during CRRT, and the results are weakly pertinent, have strong limitations, and are not easy to promote. In 2009, Tolwani et al. gave the first dosage reference as 25U / KG; in 2016, Karakala et al. gave a more relaxed range, and the first dosage can be between 25-50U / KG. When anticoagulant dosage is applied, only body weight is often considered. On the one hand, medical data is wasted, and on the other hand, it does not conform to the actual process. Therefore, when considering the application of anticoagulants, multiple indicators of the patient should be considered comprehensively. At the same time, in the face of a large amount of clinical laboratory data in reality, the first dose is oft...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H50/70G06N3/12
CPCG06N3/126G16H50/20G16H50/70
Inventor 王东赵巧凤连捷王伟
Owner DALIAN UNIV OF TECH
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