Flexible Textile-Based Pressure Sensing System Applied in the Operating Room for Pressure Injury Monitoring of Cardiac Operation Patients

The study focused on the preventive effects of the chain management model on pressure ulcers in the operating room. Sqoop big data collection module is used to collect patient information from various hospital information systems in a distributed manner. The data were from the clinical data center of the Zhongshan Hospital Xiamen University General Hospital, and 268 patients were selected as the research subjects. A chain management model is constructed, concerning the preventive measures, the management of each link, the perioperative pressure ulcer management, and the reporting of pressure ulcers. Then, the two groups were compared for the SAS and SDS scores before and after nursing, the pressure ulcer sites, pressure ulcer reporting rate, pressure ulcer staging, and nursing satisfaction. The results show that it is not that more collection modules will lead to better cluster performance and that the execution delay is caused by MapReduce requiring the JAVA virtual machine, and after reaching a certain point, the increase in the number of tasks will slow down the process, and as data size increases, DataNote has an expanded capability to analyze data. After nursing treatment, the SAS and SDS scores of the two groups of patients were significantly lower than before treatment ( P < 0.05 ). The pressure ulcers were mainly distributed in the forehead, mandible, cheeks, front chest, and knees in the two groups, and the difference between the two groups was statistically significant ( P < 0.05 ). The total satisfaction of the observation group was 93.28%, and the total satisfaction of the control group was 92.54%. The patients’ satisfaction with the chain management model was higher than that of conventional nursing.

Section: Discussionmentioning

confidence: 99%

Prevention Effects of Chain Management on Pressure Ulcers of Hospitalized Patients

Yao

Zhao

Chen

et al. 2021

“…The number of highlight areas in different images is different, but the number of highlight areas is generally small due to the existence of the Hashimoto feature. Generally, the highlight area of the image without the stripe feature is larger than that of the image with the stripe feature [ 4 , 15 ].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…The model includes a circular information network, which uses the characteristics of intelligent medical information with circular layer to study different diseases. In the experiment, LF2 in different environments is considered, which successfully proves the application of the proposed model in deep learning, but part of the discussion is not accurate enough [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning

Zhou

Liu

et al. 2022

Thyroid diseases are divided into papillary carcinoma and nodular diseases, which are very harmful to the human body. Ultrasound is a common diagnostic method for thyroid diseases. In the process of diagnosis, doctors need to observe the characteristics of ultrasound images, combined with professional knowledge and clinical experience, to give the disease situation of patients. However, different doctors have different clinical experience and professional backgrounds, and the diagnosis results lack objectivity and consistency, so an intelligent diagnosis technology for thyroid diseases based on the ultrasound image is needed in clinic, which can give objective and reliable diagnosis opinions on thyroid diseases by extracting the texture, shape, and other information of the image and assist doctors in clinical diagnosis. This paper mainly studies the intelligent ultrasonic diagnosis of papillary thyroid cancer based on machine learning, compares the ultrasonic characteristics of PTMC diagnosed by using the new ultrasound technology (CEUS and UE), and summarizes the differential diagnosis effect and clinical application value of the two technology methods for PTMC. In this paper, machine learning, diffuse thyroid image features, and RBM learning methods are used to study the ultrasonic intelligent diagnosis of papillary thyroid cancer based on machine learning. At the same time, the new contrast-enhanced ultrasound (CEUS) technology and ultrasound elastography (UE) technology are used to obtain the experimental phenomena in the experiment of ultrasonic intelligent diagnosis of papillary thyroid cancer. The results showed that 90% of the cases were diagnosed by contrast-enhanced ultrasound and confirmed by postoperative pathology. CEUS and UE have reliable practical value in the diagnosis of PTMC, and the combined application of CEUS and UE can improve the sensitivity and accuracy of PTMC diagnosis.

“…If there is a scientific and reasonable evaluation system, the logistics department can analyze the stage of construction, integrate resources, form a joint force, and concentrate on solving the main contradictions according to the needs of management. Using the smart logistics comprehensive evaluation system as the basis for evaluation, scientifically quantify the construction of smart logistics results, which can be used as a program of action guide for the construction, and play a leading, guiding, and quantitative evaluation role [1,6]. Evaluating the staged results of smart logistics and using performance appraisal as an incentive method will help to form a benign competitive environment between departments and significantly improve the level of command and logistics management [7].…”

Section: 1mentioning

confidence: 99%

Evaluation System of Smart Logistics Comprehensive Management Based on Hospital Data Fusion Technology

Yao

Wang

Ming-liang

et al. 2022

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%.