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Big data-based infusion bottle liquid change sequence recommendation method and system

A method of recommendation and infusion bottle technology, applied in image data processing, neural learning methods, drugs or prescriptions, etc., can solve problems such as low efficiency of fluid exchange, unreasonable nurses to exchange fluids, and missed calls from patients, so as to improve the efficiency of fluid exchange. Effect

Inactive Publication Date: 2021-10-29
江苏富恩日化科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a big data-based method and system for recommending the order of infusion bottle replacement, which is used to solve the problems of missed calls and late calls resulting in untimely replacement of infusion bottles when there are many patients to be replaced. When there are many patients with fluid, the nurse's fluid fluid change is unreasonable, resulting in low fluid fluid exchange efficiency

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  • Big data-based infusion bottle liquid change sequence recommendation method and system
  • Big data-based infusion bottle liquid change sequence recommendation method and system
  • Big data-based infusion bottle liquid change sequence recommendation method and system

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Embodiment 1

[0068] A method for recommending the order of infusion bottle replacement based on big data in the present invention, the main purpose of which is to realize the effective sequencing of liquid replacement operations for infusion bottles in each hospital bed, improve the efficiency of liquid replacement, and reduce the occurrence of dangerous accidents. The inventive concept is as follows: : Real-time collection of images containing infusion bottles (infusion bottles) in each ward, using neural network to process the images containing infusion bottles in each ward to obtain the original image of each infusion bottle; detecting the liquid level in the original image of each infusion bottle The dividing line, using the descending height of the interface of the liquid level within the set time interval, calculates the remaining time required for the liquid medicine in the hanging bottle to be transfused; After the required remaining time, sort according to the length of the remaini...

Embodiment 2

[0108] This embodiment provides a method for recommending the order of infusion bottle replacement based on big data. The difference from the method in Embodiment 1 is that in step S2, after the gray value of the pixel is updated, there are There are many noise points, so before determining the liquid level boundary, it is necessary to continue to process the grayscale image and filter out the noise points to obtain a more effective liquid level boundary.

[0109] Based on the above considerations, (in sub-step 4) in step S2) the specific process for continuing to process the grayscale image is:

[0110] (1) First, according to the connected domain analysis method (which is the prior art), calculate the length of the adjusted pixels that are connected to each other, and judge the set whose connected domain length is smaller than the width of the bottle as noise points, and it is impossible to be a liquid surface point. The boundary line is used to filter out such noise points....

Embodiment 3

[0115] This embodiment provides a method for recommending the order of infusion bottle replacement based on big data. The difference from the method in Embodiment 1 lies in step S3. To change the suspension bottle for the patient, it is necessary to control the priority order of the recommended liquid replacement and optimize the recommendation order.

[0116] Before sorting according to the length of the remaining time, the following steps are also included:

[0117] According to the length of the boundary line of the liquid level of the current hanging bottle, it is judged whether the current infusion progress has entered the end stage. The progress of the bottle infusion is sorted, and the shorter the remaining infusion time is, the higher the ranking is, and the sorting result is recommended as the priority of fluid replacement.

[0118] In this step, the method for judging whether the current infusion progress has entered the final stage is:

[0119] Since the diameter ...

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Abstract

The invention relates to a big data-based infusion bottle liquid change sequence recommendation method and system. The method comprises the steps of carrying out the semantic segmentation of infusion bottle images collected in real time through employing a DNN network in a neural network, accurately segmenting the original images of infusion bottles, carrying out the detection of a liquid level surface boundary which can represent the infusion progress in the original images of the infusion bottles, calculating remaining time required for infusion of the infusion bottles by using the descending height of the liquid level surface interface within the set time interval, and performing sequencing according to the length of the remaining time required for infusion of the infusion bottles, which is equivalent to sequencing of emergency degrees of liquid change according to the real infusion progress of patients on each bed. Nurses can timely master the infusion progress of each sickbed, so that the liquid changing efficiency is effectively improved. In addition, the patients do not need to continuously pay attention to the infusion progress, manpower resource consumption is reduced, and dangerous situations caused by untimely calling are reduced.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence and big data, in particular to a method and system for recommending the sequence of changing infusion bottles based on big data. Background technique [0002] At present, patients often need infusion therapy during hospitalization, and at the same time, nurses are required to perform related auxiliary work. However, each nurse station in the inpatient department is equipped with a limited number of nurses, often only equipped with two to three nurses. When there are many patients, especially when the infusion of multiple patients is intensive, the workload of the nurses will increase exponentially. Due to the large number of patients who need to change the infusion solution, the nurse can only deal with it according to the call of the patient. However, due to negligence, some patients did not call the nurse in time when the infusion bottle needs to be replaced, and the nurs...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H20/17G06T7/00G06T7/11G06T7/60G06T5/00G06N3/04G06N3/08
CPCG16H20/17G06T7/0012G06T7/11G06T7/60G06T7/62G06N3/04G06N3/08G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/30004G06T5/70
Inventor 张来娣
Owner 江苏富恩日化科技有限公司
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