Evidence map›Paper›PMID 38974364›Full record

ArticleHealth information science and systems2024

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

Ming Sheng, Shuliang Wang, Yong Zhang, Rui Hao, Ye Liang, Yi Luo, Wenhan Yang, Jincheng Wang, Yinan Li, Wenkui Zheng and 1 more

Abstract read
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In one paragraph

Article in Health information science and systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ming ShengSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China.
Shuliang WangSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China.
Yong ZhangBNRist, DCST, RIIT, Tsinghua University, Beijing, 100084 China.
Rui HaoSchool of Computer Science, International School, Beijing University of Posts and Telecommunications, Haidian District, Beijing, 100876 China.ORCID 0009-0002-6634-3156
Ye LiangSchool of Information Science and Technology, Beijing Foreign Studies University, Beijing, China.
Yi LuoSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China.
Wenhan YangSchool of Computer Science, International School, Beijing University of Posts and Telecommunications, Haidian District, Beijing, 100876 China.
Jincheng WangSchool of Computer and Information Technology, Beijing Jiaotong University, Haidian District, Beijing, 100044 China.
Yinan LiDam Safety Monitoring Center, Yellow River Engineering Consulting Co., Ltd, Zhengzhou, 450000 China.
Wenkui ZhengSchool of Software, Henan University, Kaifeng, 475004 Henan China.
Wenyao LiSchool of Software, Henan University, Kaifeng, 475004 Henan China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obtaining high-quality data sets from raw data is a key step before data exploration and analysis. Nowadays, in the medical domain, a large amount of data is in need of quality improvement before being used to analyze the health condition of patients. There have been many researches in data extraction, data cleaning and data imputation, respectively. However, there are seldom frameworks integrating with these three techniques, making the dataset suffer in accuracy, consistency and integrity. In this paper, a multi-source heterogeneous data enhancement framework based on a lakehouse MHDP is proposed, which includes three steps of data extraction, data cleaning and data imputation. In the data extraction step, a data fusion technique is offered to handle multi-modal and multi-source heterogeneous data. In the data cleaning step, we propose HoloCleanX, which provides a convenient interactive procedure. In the data imputation step, multiple imputation (MI) and the SOTA algorithm SAITS, are applied for different situations. We evaluate our framework via three tasks: clustering, classification and strategy prediction. The experimental results prove the effectiveness of our data enhancement framework.

Indexed as

Data cleaningData extractionData imputationMulti-source heterogeneous data

Identifiers

PMID38974364
PMCPMC11226589

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.