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A multi-source heterogeneous medical data enhancement framework based on lakehouse.

Ming Sheng¹, Shuliang Wang¹, Yong Zhang²

¹School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China.

Health Information Science and Systems

|July 8, 2024

Summary

This study introduces a novel data enhancement framework for medical data, integrating extraction, cleaning, and imputation. The framework improves data accuracy, consistency, and integrity for better health analysis.

Keywords:

Data cleaning Data extraction Data imputation Multi-source heterogeneous data

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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

Area of Science:

Data Science
Medical Informatics
Machine Learning

Background:

High-quality datasets are crucial for medical data analysis.
Existing research often addresses data extraction, cleaning, and imputation separately.
A lack of integrated frameworks hinders medical data accuracy, consistency, and integrity.

Purpose of the Study:

To propose a novel, integrated data enhancement framework for multi-source heterogeneous medical data.
To address limitations in current data preprocessing techniques in the medical domain.

Main Methods:

Developed a lakehouse-based framework (MHDP) encompassing data extraction, cleaning, and imputation.
Employed a data fusion technique for multi-modal and multi-source data extraction.
Introduced HoloCleanX for interactive data cleaning and utilized Multiple Imputation (MI) and SAITS for data imputation.

Main Results:

The MHDP framework demonstrated effectiveness in enhancing medical datasets.
Evaluated performance across clustering, classification, and strategy prediction tasks.
Experimental results confirmed the framework's ability to improve data quality.

Conclusions:

The proposed MHDP framework offers an effective solution for medical data enhancement.
Integration of data extraction, cleaning, and imputation significantly improves dataset quality.
The framework facilitates more reliable health condition analysis through improved data.