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Statistical Software for Data Analysis and Clinical Trials

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Smart Extraction and Analysis System for Clinical Research.

Muhammad Afzal¹, Maqbool Hussain¹, Wajahat Ali Khan¹

¹1 Ubiquitous Computing Lab, Department of Computer Science and Engineering, Kyung Hee University , Yongin, South Korea .

Telemedicine Journal and E-Health : the Official Journal of the American Telemedicine Association

|October 27, 2016

Summary

This summary is machine-generated.

A new Smart Extraction and Analysis System (SEAS) automates clinical data preparation for research. This system significantly reduces manual data entry time and improves information extraction accuracy from electronic health records (EHRs).

Keywords:

cancer survival analysis clinical research e-health information extraction pattern recognition

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Area of Science:

Biomedical Informatics
Clinical Research Informatics

Background:

Electronic health records (EHRs) offer vast potential for clinical research.
Data preparation, structuring, and sharing remain significant challenges for EHR data utilization.
Smart systems are needed to streamline the use of EHR data in research.

Purpose of the Study:

To develop an automated system for extracting and analyzing clinical data from EHRs.
To improve the efficiency and accuracy of clinical data preparation for research.
To enable predictive survival analysis using EHR data.

Main Methods:

Developed the Smart Extraction and Analysis System (SEAS) with two subsystems: Information Extraction System (IES) and Survival Analysis System (SAS).
IES utilizes a novel permutation-based pattern recognition method for extracting information from unstructured clinical documents.
SAS employs a Classification and Regression Tree (CART)-based prediction model for survival analysis.

Main Results:

SEAS achieved 99% accuracy for semistructured text and 97% for unstructured text in information extraction.
Automated extraction reduced manual data entry time by 75% without compromising accuracy.
Analysis of head and neck cancer data revealed significant survival correlations with disease stage and lifestyle risk factors.

Conclusions:

SEAS offers a smart, automated alternative to costly and time-consuming manual data preparation and analysis.
The system reduces human resource expenditure on manual tasks in clinical research.
SEAS facilitates efficient utilization of EHR data for clinical research and survival prediction.