Rehabilitation of Sepsis Patients with Acute Kidney Injury Based on Intelligent Medical Big Data

The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance ( P < 0.05 ). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance ( P < 0.05 ). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.

A Brief Survey on Big Data in Healthcare

This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition of big data, the components of big data, medical big data sources, used big data technologies in present, and big data analytics in healthcare have been examined under the different titles. Also, the historical development process of big data analytics has been mentioned. As a known big data analytics technology, Apache Hadoop technology and its core components with tools have been explained briefly. Moreover, a glance of some of the big data analytics tools or platforms apart from Hadoop eco-system were given. The main goal is to help researchers or specialists with giving an opinion about the rising importance of used big data analytics in healthcare systems.

Prediction of the Collapse of Necrotic Femoral Head by CT and X-Ray Examinations before Hip Replacement Based on Intelligent Medical Big Data

It was to explore the effect of the CT and X-ray examinations before the hip replacement to predict the collapse of the necrotic femoral head under the classification of medical big data based on the decision tree algorithm of the difference grey wolf optimization (GWO) and provide a more effective examination basis for the treatment of patients with the osteonecrosis of the femoral head (ONFH). From January 2019 to January 2021, a total of 152,000 patients with ONFH and hip replacement in the tertiary hospitals were enrolled in this study. They were randomly divided into two groups, the study sample-X group (X-ray examination results) and based-CT group (CT examination results)—76,000 cases in each group. The actual measurement results of the femoral head form the gold standard to evaluate the effect of the two groups of detection methods. The measurement results of X-ray and CT before hip replacement are highly consistent with the detection results of the physical femoral head specimens, which can effectively predict the collapse of ONFH and carry out accurate staging. It is worthy of clinical promotion.

Clinical Characteristics and Economic Burden of Asthma in China: A Multicenter Retrospective Study

Background: Asthma is a common chronic airway inflammation that produces a healthcare burden on the economy.Objective: To obtain a better understanding of the clinical status and disease burden of patients with asthma in China. Methods: A retrospective study based on the computerized medical records in the Jinan Health Medical Big Data Platform between 2011 and 2019 (n = 31,082) was carried out. The asthma severity of each patient was assessed retrospectively and categorized as mild, moderate, or severe according to GINA 2018. Results: The results revealed that the majority (75.0%) of patients suffered from mild asthma. Patients treated with ICS/LABA at emergency department visits had lower frequencies of exacerbations in the following year compared with non-ICS/LABA treated patients.The incidence rates for 1 exacerbation are 14.49% vs 15.01 for the patients treated with ICS/LABA and without. The rates are 11.94% and 19.12% for 2 exacerbations above. The numbers are 6.51% vs 12.92% for 3 exacerbations above. The rates are 4.10% vs 9.35% for 4 exacerbations above. Also the difference got the statistical significance (p <0.001), COPD and GRED, two comorbidities related to asthma, were risk factors for asthma exacerbation. Finally, patients who suffered from exacerbations produced a heavier economic burden compared to the patients who never suffered exacerbations(mean costs are ￥3,339.67 vs ￥968.45 separately). Conclusions: These results provide a reference for clinicians and patients to obtain a better treatment and therapy strategy management for asthma sufferers.

Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data

The ultrasonic imaging research of nursing care for preventing and treating clinical infection of hemodialysis patients based on smart medical big data is studied. 100 hemodialysis patients were selected from May 2019 to May 2020. The patients were randomly divided into the observation group and routine group with 50 cases in each group. The PWV of common carotid artery was measured by ultrasonic rapid imaging technology, including BS value at the beginning of systole and ES value at the end of systole. According to the effect of preventive nursing intervention of intelligent medical treatment, the MHD group adopted preventive nursing intervention, while the routine group adopted traditional nursing service. The infection rate and quality of life score of patients in both groups were evaluated. The results showed that there were significant differences in BS and ES values between the MHD group and PWV in the normal group ( P < 0.05 ). There was no significant difference in BS value and ES value between MHD patients with plaque and those without plaque ( P > 0.05 ). It is proved that the ultrafast ultrasound imaging technology is safe, simple, noninvasive, nonradioactive, and fast and can automatically and accurately detect carotid PWV. It is expected to become a new imaging method for quantitative evaluation of arteriosclerosis degree in MHD patients. Preventive nursing intervention can reduce the incidence of infection in hemodialysis patients and improve their quality of life. Smart medical treatment has brought us a lot of convenience. As patients, we should change our concept, actively participate in it, and contribute to the development of smart medical treatment.

Research on Design of Fog Computing Optimization Model for Medical Big Data

Targeted at the current issues of communication delay, data congestion, and data redundancy in cloud computing for medical big data, a fog computing optimization model is designed, namely an intelligent front-end architecture of fog computing. It uses the network structure characteristics of fog computing and “decentralized and local” mind-sets to tackle the current medical IoT network’s narrow bandwidth, information congestion, heavy computing burden on cloud services, insufficient storage space, and poor data security and confidentiality. The model is composed of fog computing, deep learning, and big data technology. By full use of the advantages of WiFi and user mobile devices in the medical area, it can optimize the internal technology of the model, with the help of classification methods based on big data mining and deep learning algorithms based on artificial intelligence, and automatically process case diagnosis, multi-source heterogeneous data mining, and medical records. It will also improve the accuracy of medical diagnosis and the efficiency of multi-source heterogeneous data processing while reducing network delay and power consumption, ensuring patient data privacy and safety, reducing data redundancy, and reducing cloud overload. The response speed and network bandwidth of the system have been greatly optimized in the process, which improves the quality of medical information service.