| Literature DB >> 35469219 |
Yong Liang1, Yugeng Liu1, Bo Liu1, Aimin Xu1, Junyu Wang2.
Abstract
The aim of this study was to explore the application of process reengineering integration in trauma first aid based on deep learning and medical information system. According to the principles and methods of process reengineering, based on the analysis of the problems and causes of the original trauma first aid process, a new set of trauma first aid integration process is established. The Deep Belief Network (DBN) in deep learning is used to optimize the travel path of emergency vehicles, and the accuracy of travel path prediction of emergency vehicles under different environmental conditions is analyzed. DBN is applied to the surgical clinic of the hospital to verify the applicability of this method. The results showed that in the analysis of sample abscission, the abscission rates of the two groups were 2.23% and 0.78%, respectively. In the analysis of the trauma severity (TI) score between the two groups, more than 60% of the patients were slightly injured, and there was no significant difference (P > 0.05). In the comparative analysis of treatment effect and family satisfaction between the two groups, the proportion of rehabilitation patients in the experimental group (55.91%) was significantly better than that in the control group, and the satisfaction of the experimental group (7.93 ± 0.59) was significantly higher than that of the control group (5.87 ± 0.43) (P < 0.05). Therefore, integrating Wireless Sensor Network (WSN) measurement and process reengineering under the medical information system provides feasible suggestions and scientific methods for the standardized trauma first aid.Entities:
Mesh:
Year: 2022 PMID: 35469219 PMCID: PMC9034939 DOI: 10.1155/2022/8789920
Source DB: PubMed Journal: Comput Math Methods Med ISSN: 1748-670X Impact factor: 2.809
Figure 1Roadmap for building an integrated trauma emergency process.
Figure 2Traditional trauma first aid flowchart.
Figure 3New process.
Figure 4The basic structures of the restricted Boltzmann machine and the DBN model.
Figure 5First aid route optimization process.
Comparison of general conditions and injuries between the two groups of patients (n (%)).
| Variable | Category | Control group ( | Test group ( |
|
|
|---|---|---|---|---|---|
| Gender | Male | 91 (69.47) | 93 (73.23) | 0.274 | 0.605 |
| Female | 40 (30.53) | 34 (26.77) | |||
| Cause of injury | Car accident | 94 (71.76) | 91 (72.95) | 0.613 | 0.716 |
| Fall injury | 23 (17.56) | 19 (71.65) | |||
| Other injuries | 14 (10.69) | 17 (13.39) |
Comparison of wound index scores between the two groups (n (%)).
| Score | Control group ( | Test group ( |
|
|
|---|---|---|---|---|
| 2~9 | 79 (60.31) | 77 (60.63) | 0.469 | 0.931 |
| 10~17 | 32 (24.43) | 33 (25.98) | ||
| 17~20 | 12 (9.16) | 12 (9.45) | ||
| 20 or more points | 8 (6.11) | 5 (3.94) |
Figure 6Prediction results of the emergency route based on deep learning: (a) accuracy of T0; (b) accuracy of W; (c) accuracy of V.
Comparison of two groups of patients admitted to the shunt time (n (%)).
| Group | Within 10 min | Within 20 minutes | Within 30 minutes |
|---|---|---|---|
| Experimental group ( | 83 (65.35) | 38 (29.92) | 6 (4.72) |
| Control group ( | 56 (42.75) | 65 (49.62) | 10 (7.63) |
|
| 10.729 | ||
|
| 0.001 | ||
Comparison of treatment effects between the two groups of patients (n (%)).
| Group | Get well | Better | Invalid |
|---|---|---|---|
| Experimental group ( | 71 (55.91) | 42 (33.07) | 14 (11.02) |
| Control group ( | 44 (33.59) | 71 (54.20) | 16 (12.21) |
|
| 10.667 | ||
|
| 0.001 | ||
Comparison of family satisfaction scores between the two groups of patients (n ± x).
| Group | Medical work attitude | First aid program | Treatment effect | Overall score |
|---|---|---|---|---|
| Experimental group ( | 2.71 ± 0.53 | 3.01 ± 0.27 | 3.65 ± 0.35 | 7.93 ± 0.59 |
| Control group ( | 0.23 ± 0.16 | 2.11 ± 0.54 | 2.52 ± 0.51 | 5.87 ± 0.43 |
|
| 5.217 | 3.264 | 2.247 | 2.551 |
|
| ≤0.01 | ≤0.01 | 0.001 | 0.001 |