scholarly journals Application Analysis Based on Big Data Technology in Stroke Rehabilitation Nursing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
WeiHua Xu ◽  
LiangJin Liu ◽  
JiuXia Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Ischemic Stroke ◽  
Clinical Pathway ◽  
Clinical Pathways ◽  
Treatment Plan ◽  
Science And Technology ◽  
Medical Institution ◽  
Level 3 ◽  
Big Data Technology

According to the statistical analysis, the incidence of stroke disease has gradually increased, particularly in recent years, which poses a huge threat to the safety of human life. Due to the advancement in science and technology specifically big data and sensors, a new research dome known as data mining technology has been introduced, which has the potential value from the perspective of large amount of data analysis. Information has become a new trend of science and technology, and data mining has been used in various application areas to analyze and predict strokes at home and abroad. In this study, big data technology is utilized to collect potential information and explores clinical pathways of level-3 rehabilitation in certain regions of China. Moreover, application effects of data mining in the rehabilitation of patients with the first ischemic stroke have been evaluated and reported. For this purpose, fifty (50) first-time ischemic stroke patients have been screened through big data and were nonartificially assigned to level-3 clinical pathway and conventional rehabilitation groups, respectively, specifically through software. The first group of patients enters the clinical path of the corresponding level according to the way of three-level referral. These patients were analyzed based on the collected results of completing the unified rehabilitation treatment plan of the three-level rehabilitation medical institution in the patient record form. The second group was selected according to the routine rehabilitation model and method of the medical institution where the patients visited were divided into four stages: before treatment, three weeks after treatment, nine weeks after treatment, and seventeen weeks after treatment. For this purpose, a simplified Fugl-Meyer analysis (FMA), recording of various functions of limb movement, and modified Barthel index (MBI) scale were used to analyze and evaluate the ability of daily activities and compare their effects. The final results showed that FMA and MBI scores of the two groups were improved in the three stages after treatment. The FMA and MBI scores of the clinical pathway group on 3rd and 9th weekends were significantly different from those of the conventional rehabilitation group (which is p < 0.05 ). Moreover, difference in FMA and MBI scores between the two at the 17th weekend was not significant. The total cost of the clinical pathway group, particularly at the ninth weekend, was higher than that of the conventional rehabilitation group, but the cost-benefit ratio was better and the incidence of complications was lower than that of the other group.

Value of thoracic multidisciplinary clinic (TMDC) in determining appropriate treatment of stage III non-small cell lung cancer (NSCLC).

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 82-82 ◽  
Author(s):  
Eliot L Friedman ◽  
Michael F Szwerc ◽  
Robert Kruklitis ◽  
Michael J Weiss
Keyword(s):  
Clinical Pathway ◽  
Initial Treatment ◽  
Clinical Pathways ◽  
Treatment Plan ◽  
Clinical Status ◽  
Stage Iii ◽  
Definitive Treatment ◽  
Mediastinal Staging ◽  
Multidisciplinary Clinic ◽  
Stage Iii Nsclc

82 Background: Treatment of stage III NSCLC involves surgery, radiation therapy and chemotherapy. Treatment depends on the size and location of the primary tumor and lymph nodes as well as clinical status of the patient. Evaluation of these patients should take place in a multidisciplinary clinic, where treating physicians and pulmonary medicine provide a unified treatment plan. Methods: All patients with Stage III NSCLC seen at the Lehigh Valley Health Network (LVHN) between March of 2010 and March of 2012 were analyzed retrospectively. We compared initial treatment of out-patients seen in our TMDC with those out-patients seen outside the TMDC. Results: Thirty-five patients were seen in TMDC (34 treated at LVHN) and 44 patients were seen outside TMDC (34 treated at LVHN). Eleven patients were treated elsewhere or were not treatable. Of patients with stage III NSCLC, 37.5% were seen in TMDC year 1 (March 2010 – March 2011) compared to 61% of patients year 2 (March 2011 – March 2012) (p = 0.05). Patients were seen by physicians from at least two specialties 100% of the time when seen in TMDC, but only 64.7% of the time when seen outside TMDC (p < 0.001). Mediastinal staging (EBUS or mediastinoscopy) was performed more frequently in patients seen in TMDC; 58.9% compared to 23.5% outside TMDC (p = 0.009). The LVHN clinical pathway for stage III NSCLC recommends initial therapy with concomitant chemoradiation, either in the neo-adjuvant setting or as definitive treatment. Eighty-eight percent of patients seen in TMDC followed our clinical pathway while 46% of patients seen outside TMDC conformed to the clinical pathway (p < 0.001). The time from first contact with a treating physician to initiation of treatment was reduced by almost 30% (29.03 days outside TMDC; 20.62 days at TMDC). Conclusions: All patients with stage III NSCLC should be seen in a multidisciplinary setting. At LVHN we saw an increase in these patients being referred to our TMDC over time. These patients were more likely to have mediastinal staging and enjoyed quicker initiation of their therapy. They were more likely to have at least two physicians involved in their initial treatment plan and were more likely to conform to our clinical pathways.

scholarly journals Underpinnings of Big Data Analytics and Its Applications

2020 ◽  
pp. 75-81
Author(s):  
Anand Kumar Pandey ◽  
Rashmi Pandey ◽  
Ashish Tripathi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Issues ◽  
New Developments ◽  
New Ideas ◽  
Analytical Assessment ◽  
Data Analytic ◽  
Big Data Technology

Big data and Data Mining are co-related to each other and also emphasize the phenomena of extracting and analysis useful data from considerable database. The concept of Big Data analytics plays a very significant role in several fields, such as Data Mining, Education and Training, cloud computing, E-commerce, healthcare and life science, Banking and Agriculture. Big data Analytic is a technique for looking at big set of data to expose hidden patterns. A large amount of data is continuously generated every day using modern information system and technologies. As a result this paper provides a platform to investigate applications of big data at various stages. In future, it come forward to be a required for an analytical assessment of new developments in the big data technology. In addition, it also explores a new and suitable outlook for researchers to expand the solution, based on the literature survey, challenges, new ideas and open research issues.

scholarly journals Big Data Technology Application in Mechanical Intelligent Fault Diagnosis

2021 ◽  
Vol 2066 (1) ◽  
pp. 012064
Author(s):  
Huiteng Cao
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Big Data ◽  
Fault Diagnosis ◽  
Machining Process ◽  
Mechanical Equipment ◽  
Data Mining Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Mining Algorithm ◽  
Big Data Technology

Abstract With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.

scholarly journals Research on the Personal Privacy in the Health Big Data

2020 ◽  
Vol 214 ◽  
pp. 03022
Author(s):  
Cangcang JIA ◽  
Han LIU
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Conflicts Of Interest ◽  
Personal Privacy ◽  
Conflict Of Interests ◽  
Data Prediction ◽  
Data Application ◽  
Digital Monitoring ◽  
Big Data Application ◽  
Big Data Technology

Data mining, data prediction and all-round digital monitoring of health big data make the dilemma of personal privacy control prominent. The weakening of the control of personal privacy by big data technology, the people’s data belief, the diversity of interests and the conflict of interests are the main causes of the problem of personal privacy in the context of the application of health big data. Therefore, in the application of health big data, the solution to the problem of personal privacy in the context of health big data application is to enhance the value transparency of big data technology, return and reshape humanism, and explore common values to reduce conflicts of interest.

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