Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm

Abstract

A large amount of patient information has been gathered in Electronic Health Records (EHRs) concerning their conditions. An EHR, as an unstructured text document, serves to maintain health by identifying, treating, and curing illnesses. In this research, the technical complexities in extracting the clinical text data are removed by using machine learning and natural language processing techniques, in which an unstructured clinical text data with low data quality is recognized by Halve Progression, which uses Medical-Fissure Algorithm which provides better data quality and makes diagnosis easier by using a cross-validation approach. Moreover, to enhance the accuracy in extracting and mapping clinical text data, Clinical Data Progression uses Neg-Seq Algorithm in which the redundancy in clinical text data is removed. Finally, the extracted clinical text data is stored in the cloud with a secret key to enhance security. The proposed technique improves the data quality and provides an efficient data extraction with high accuracy of 99.6%.

Biomedical Application of Identified Biomarkers Gene Expression Based Early Diagnosis and Detection in Cervical Cancer with Modified Probabilistic Neural Network

“…Table 1 shows the performance metrics of the proposed work. In order to demonstrate the diagnostic capabilities of a binary classifier system when the discrimination threshold of the system is modified [ 5 ], Receiver Operating Characteristic (ROC) curves are plotted against the threshold value. The ROC curve may be produced and examined when the TPR and the FPR are plotted against each other at different threshold values [ 8 , 25 ].…”

“…(4) Repeat steps 2-3 as many times as required until there is no change in the medoids' appearance or function. (5) Recognizing clusters in the form of a dendrogram (tree of life diagram). ( 6) Using the gene dataset that has been provided to you, create the most informative modules that you can.…”

“…Table 1 shows the performance metrics of the proposed work. In order to demonstrate the diagnostic capabilities of a binary classifier system when the discrimination threshold of the system is modified [5], Receiver Operating Characteristic (ROC) curves are plotted against the threshold value.…”

“…The use of optimization techniques has become more popular among researchers in order to help them in selecting the qualities that they want to use in their investigations. Part of the proposed research includes the introduction of a feature selection strategy based on the metaheuristic algorithm [ 5 ], which is used as an optimal search tool to choose a subset of characteristics from a larger range of characteristics.…”

Potential for the use of large unstructured data resources by public innovation support institutions

“…For effective analysis of this type of data and their integration with structured data, many complex IT systems are being created [13]. In the case of biomedical data, it is important not only to improve the quality of data analysis, but also to protect the data and limit access to it [14]. The analytical tools we describe require adequate computing power, but this allows the time of data processing to be shortened.…”