Method for predicting hospitalization stress injury healing based on big data mining model

A model prediction and pressure technology, applied in the field of predicting the healing of hospitalized pressure injuries based on big data mining models, can solve the problems of lack of comprehensive evaluation system and incomplete prognosis of pressure injury, and achieve the effect of promoting healing

Pending Publication Date: 2020-03-27
NANTONG UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Domestic research also shows that the change of PUSH score is not completely consistent with the prognosis of pressure injury
[0005] According to the above two points, there is still a lack of comprehensive evaluation system to predict

Method used

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  • Method for predicting hospitalization stress injury healing based on big data mining model
  • Method for predicting hospitalization stress injury healing based on big data mining model
  • Method for predicting hospitalization stress injury healing based on big data mining model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Example 1: The patient is less than 75 years old, the hospitalization period is 3 days, the latest albumin value is 35, the Braden score is 15, and the partial cortical defect with exposed dermis is identified as a second-stage pressure injury patient with an area of ​​0.4cm 2 ;

[0042] According to the regression equation predicting the probability of healing in patients with pressure injuries:

[0043] Logit(P)=-4.073+0.027*3+0.062*35+0.186*15

[0044] =-4.073+0.081+2.17+2.79

[0045] =96.8%

[0046] The model predicts that the probability of healing is 96.8%, which is difficult for low-risk patients to heal. The patient recovered well when he was actually discharged from the hospital.

Embodiment 2

[0047] Example 2: The patient is older than 75 years old, the hospital stay is 90 days, the latest albumin value is 60, the Braden score is 12, and the full-thickness skin and tissue defect is identified as a fourth-stage pressure injury patient with an area of ​​about 4cm 2 ;

[0048] According to the regression equation predicting the probability of healing in patients with pressure injuries:

[0049] Logit(P)=-4.073-0.475+0.027*90+0.062*60+0.186*12-2.362-1.188

[0050] =-4.548+2.43+3.72+2.232-3.55

[0051] =28.4%

[0052] After the model prediction, the probability of healing is 28.4%, which is difficult for high-risk patients. In fact, the wound was not easy to heal for a long time, and he was discharged from the hospital with sores.

Embodiment 3

[0053] Example 3: The patient is less than 75 years old, the hospitalization period is 80 days, the latest albumin value is 57, the Braden score is 12, full-thickness skin defect, fat, granulation tissue or skin involution can be seen in the defect, which is identified as three-stage pressure Injured patient with an area of ​​2.1 cm 2 ;

[0054] Regression equation predicting the probability of healing in patients with pressure injuries: 7.623

[0055] Logit(P)=-4.073+0.027*80+0.062*57+0.186*12-1.326-1.124

[0056] =-4.073+2.16+3.534+2.232-3.55

[0057] =30.3%

[0058] According to the prediction of the model, the probability of healing is 30.3%, making it difficult for high-risk patients to heal. In fact, the wound was not easy to heal for a long time, and he was discharged from the hospital with sores.

[0059] This prediction model predicts the healing risk of clinical pressure injury by extracting the general characteristics, disease-related characteristics, and blood...

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Abstract

The invention discloses a method for predicting hospitalization stress injury healing based on a big data mining model. According to the prediction model, the general characteristics, disease-relatedcharacteristics and blood routine examination and blood biochemical examination report results of hospitalized pressure injury patients are extracted, and the healing risk of the clinical pressure injury is predicted. According to the method and the model, the refractory stress injury can be recognized in the early stage through a simple and effective visual scoring system, and the outcome and prognosis of the refractory stress injury are judged; furthermore, related intervention can be performed as soon as possible, a layering basis can be provided for personalized intervention strategies ofpressure injury patients with different healing risks in the later period, and therefore, the healing of clinical pressure injury is promoted.

Description

technical field [0001] The invention relates to the technical field of clinical pressure injury prediction, in particular to a method for predicting hospitalized pressure injury healing based on a big data mining model. Background technique [0002] A pressure injury is defined as a localized injury to the skin and / or underlying soft tissue, usually over a bony prominence or where the skin contacts a medical device. With the acceleration of the global aging process, the increase of patients with chronic diseases and the prolongation of their living time with the disease, the number of bedridden patients continues to increase, resulting in a continuous increase in the high-risk population of pressure injury, coupled with the existence of inevitable pressure injury, resulting in The incidence of pressure injuries remains high. Once pressure injury occurs, its clinical treatment is relatively difficult and expensive, which brings a heavy economic burden to both patients and so...

Claims

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Application Information

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IPC IPC(8): G16H50/70G06F17/18
CPCG16H50/70G06F17/18
Inventor 陈宏林杜琳查曼丽蔡季煜宋依平
Owner NANTONG UNIVERSITY
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