Classification of Medical Data using Character-level CNN

2020 ◽  
Author(s):  
Kazuteru Miyazaki
Keyword(s):  
Medical Data

Medical Reports Analysis Using Natural Language Processing for Disease Classification

2020 ◽  
pp. 155-172
Author(s):  
Sumathi S. ◽  
Indumathi S. ◽  
Rajkumar S.
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Text Classification ◽  
Medical Data ◽  
Disease Classification ◽  
Human Interaction ◽  
Time Consumption ◽  
Medical Reports ◽  
Medical Documents

Text classification in medical domain could result in an easier way of handling large volumes of medical data. They can be segregated depending on the type of diseases, which can be determined by extracting the decisive key texts from the original document. Due to various nuances present in understanding language in general, a requirement of large volumes of text-based data is required for algorithms to learn patterns properly. The problem with existing systems such as MedScape, MedLinePlus, Wrappin, and MedHunt is that they involve human interaction and high time consumption in handling a large volume of data. By employing automation in this proposed field, the large involvement of manpower could be removed which in turn speeds up the process of classification of the medical documents by which the shortage of medical technicians in third world countries are addressed.

scholarly journals Automated classification of peripheral distal by-pass geometries reconstructed from medical data

2005 ◽  
Vol 38 (1) ◽  
pp. 47-62 ◽  
Author(s):  
S. Giordana ◽  
S.J. Sherwin ◽  
J. Peiró ◽  
D.J. Doorly ◽  
Y. Papaharilaou ◽  
...  
Keyword(s):  
Medical Data ◽  
Automated Classification

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

A Hybrid Approach for Cases Classification of Medical Data

2014 ◽  
Vol 496-500 ◽  
pp. 1965-1970
Author(s):  
Xiao Yu Chen ◽  
Bo Liu ◽  
Xin Xia
Keyword(s):  
Feature Selection ◽  
Hybrid Approach ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Emission Computed Tomography ◽  
Proton Emission ◽  
Clinical Theory ◽  
Hybrid Classification ◽  
C5.0 Decision Tree

Classification of cases has been widely applied in medicine, and it is helpful to disease diagnosis to a great extent. At present, the classification of medical cases is performed by physicians subjectively based on clinical theory and knowledge, which may hinder the diagnosis and treatment in some extent. In this paper, a hybrid classification approach (HCA) is proposed for medical data, it consists of two parts, including feature selection and classification. In feature selection, critical features are selected from the original features through linear correlation. Based on the selected features, cases are classified by C5.0 decision tree. And the proposed approach is evaluated through four medical datasets of diabetes, cardiac Single Proton Emission Computed Tomography (SPECT) images, lung cancer, and hepatitis survival for demonstration. On the four datasets, HCA shows a better construction for obviously higher classification accuracies, and it also outperforms some typical integrated classification methods.

scholarly journals CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

2019 ◽  
Vol 8 (10) ◽  
pp. 2256-2262
Keyword(s):  
Classification Performance ◽  
Medical Data ◽  
Divergence Measure ◽  
Data Set ◽  
It Convergence ◽  
Class Level ◽  
Data Points ◽  
Medical Data Classification ◽  
Different Dimensions

The problem of medical data classification is analyzed and the methods of classification are reviewed in various aspects. However, the efficiency of classification algorithms is still under question. With the motivation to leverage the classification performance, a Class Level disease Convergence and Divergence (CLDC) measure based algorithm is presented in this paper. For any dimension of medical data, it convergence or divergence indicates the support for the disease class. Initially, the data set has been preprocessed to remove the noisy data points. Further, the method estimates disease convergence/divergence measure on different dimensions. The convergence measure is computed based on the frequency of dimensional match where the divergence is estimated based on the dimensional match of other classes. Based on the measures a disease support factor is estimated. The value of disease support has been used to classify the data point and improves the classification performance.

scholarly journals Machine Learning for Classification of Postoperative Patient Status Using Standardized Medical Data

2021 ◽  
pp. 106583
Author(s):  
Takanori Yamashita ◽  
Yoshifumi Wakata ◽  
Hideki Nakaguma ◽  
Yasunobu Nohara ◽  
Shinj Hato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Postoperative Patient ◽  
Patient Status
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