scholarly journals Research on Diagnostic Information of Smart Medical Care Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhihai Xu ◽  
Donglin Shi ◽  
Zhiwei Tu
Keyword(s):  
Big Data ◽  
Medical Care ◽  
Medical Diagnosis ◽  
Distributed Storage ◽  
Transmission Function ◽  
Processing System ◽  
Transmission Time ◽  
Medical Field ◽  
Medical Big Data ◽  
Data Acquisition Module

The medical field has gradually become intelligent, and information and the research of intelligent medical diagnosis information have received extensive attention in the medical field. In response to this situation, this paper proposes a Hadoop-based medical big data processing system. The system first realized the ETL module based on Sqoop and the transmission function of unstructured data and then realized the distributed storage management function based on HDFS. Finally, a MapReduce algorithm with variable key values is proposed in the data statistical analysis module. Through simulation experiments on the system modules and each algorithm, the results show that because the traditional nondistributed big data acquisition module transmits the same scale of data, it consumes more than 3200 s and the transmission time exceeds 3000 s. The time consumption of smart medical care under the 6G protocol is 150 s, the transmission time is 146 s, and the time is reduced to 1/10 of the original. The research of intelligent medical diagnosis information based on big data has good rationality and feasibility.

Design and Development of a Medical Big Data Processing System Based on Hadoop

2015 ◽  
Vol 39 (3) ◽  
Author(s):  
Qin Yao ◽  
Yu Tian ◽  
Peng-Fei Li ◽  
Li-Li Tian ◽  
Yang-Ming Qian ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing System ◽  
Data Processing System ◽  
Design And Development ◽  
Big Data Processing ◽  
Medical Big Data

Embedded medical big data network and nursing intervention of aspiration pneumonia

2021 ◽  
Vol 80 ◽  
pp. 103659
Author(s):  
Meiling Guo ◽  
Chao Bian ◽  
Lingcheng Meng ◽  
Yan Wang
Keyword(s):  
Big Data ◽  
Aspiration Pneumonia ◽  
Nursing Intervention ◽  
Data Network ◽  
Medical Big Data

scholarly journals A Real Time Processing system for big data in astronomy: Applications to HERA

2021 ◽  
pp. 100489
Author(s):  
Paul La Plante ◽  
P.K.G. Williams ◽  
M. Kolopanis ◽  
J.S. Dillon ◽  
A.P. Beardsley ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Processing System ◽  
Real Time Processing ◽  
Time Processing

scholarly journals Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianhui Wu ◽  
Lu Zhang ◽  
Sufeng Yin ◽  
Haidong Wang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Big Data ◽  
Differential Diagnosis ◽  
Decision Tree ◽  
Cell Morphology ◽  
Bp Neural Network ◽  
Data Platform ◽  
New Ideas ◽  
Medical Big Data

The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo-MDS and AA and analyzes the characteristics of the optimal model misjudgment data.

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