ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
The Impact of Medical Big Data Anonymization on Early Acute Kidney Injury Risk Prediction.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Ethical Considerations for the Use of Social Media in the Human Subjects Research Setting.Journal of medical Internet research · 2025Article
- The Costs of Anonymization: Case Study Using Clinical Data.Journal of medical Internet research · 2024Article
- Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization.JMIR public health and surveillance · 2022Article
- Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence enabled medical big data analysis has the potential to revolutionize medical practice from diagnosis and prediction of complex diseases to making recommendations and resource allocation decisions in an evidence-based manner. However, big data comes with big disclosure risks. To preserve privacy, excessive data anonymization is often necessary, leading to significant loss of data utility. In this paper, we develop a systematic data scrubbing procedure for large datasets when key variables are uncertain for re-identification risk assessment and assess the trade-off between anonymization of electronic health record data for sharing in support of open science and performance of machine learning models for early acute kidney injury risk prediction using the data. Results demonstrate that our proposed data scrubbing procedure can maintain good feature diversity and moderate data utility but raises concerns regarding its impact on knowledge discovery capability.
Indexed as
Identifiers
32477684PMC7233037W3030080455What OpenQuestion holds
Registered trials
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.