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Updated: Oct 29, 2025

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

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Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine

Takuma Oda¹, Shih-Wei Chiu¹, Takuhiro Yamaguchi¹

¹Division of Biostatistics, Tohoku University Graduate School of Medicine, Sendai-city, Miyagi Prefecture, Japan.

Methods of Information in Medicine

|July 8, 2021

Summary

This study presents a semi-automated method for converting legacy data to Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) format, improving efficiency over manual processes.

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Area of Science:

Biomedical Informatics
Data Management in Clinical Research

Background:

Legacy data conversion to standardized formats is crucial for clinical research.
Manual conversion processes are time-consuming and prone to errors.

Purpose of the Study:

To develop a semi-automated process for converting legacy data to CDISC SDTM format.
To enhance the efficiency and accuracy of data standardization.

Main Methods:

Employed human verification combined with data normalization, distributed representation (Gestalt pattern matching, Doc2vec), and supervised machine learning.
Evaluated decision tree, random forest, gradient boosting, neural network, and ensemble algorithms.

Main Results:

The neural network algorithm achieved the highest accuracy.
The combined approach successfully enabled semi-automated conversion to CDISC SDTM format.

Conclusions:

A semi-automated process for legacy data conversion to CDISC SDTM format was successfully developed.
This method offers improved efficiency compared to traditional manual approaches.