ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
ARX--A Comprehensive Tool for Anonymizing Biomedical Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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.
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.
Who cites it
19 citing papers in PubMed.
- Quantifying the Impact of Anonymization-Induced Clinical Data Quality Loss: Methodological Quantitative Case Study Using Primary Diagnosis Codes and Hospital Length of Stay.JMIR medical informatics · 2026Article
- LLM research on public biosignals data is needed to protect patients.NPJ digital medicine · 2026Review
- Study Protocol for HeartMagic: A Prospective Observational Cohort Characterizing Subtypes of Heart Failure With Preserved Ejection Fraction.Journal of the American Heart Association · 2025Article
- Monitoring changes in vitamin D levels during the COVID-19 pandemic with routinely-collected laboratory data.Nature communications · 2025Article
- Comparison of Three Anonymization Tools for a Health Fitness Study.Scientific data · 2025Article
- Possibilities for secondary data use of electronic health records with WiseSpace de-identification.Frontiers in medicine · 2025Article
- Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.BMC medical informatics and decision making · 2024Article
- FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards.Frontiers in genetics · 2023Review
- Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values.Journal of personalized medicine · 2022Article
- Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study.JMIR medical informatics · 2021Review
- Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study.Journal of medical Internet research · 2021Article
- Big Data Warehouse for Healthcare-Sensitive Data Applications.Sensors (Basel, Switzerland) · 2021Article
- Data Integration for Future Medicine (DIFUTURE).Methods of information in medicine · 2018Article
- MIRACUM: Medical Informatics in Research and Care in University Medicine.Methods of information in medicine · 2018Article
- Phenotype Detection Registry System (PheDRS) - Implementation of a Generalizable Single Institution Clinical Registry Architecture.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Open University Learning Analytics dataset.Scientific data · 2017Article
- Efficient and effective pruning strategies for health data de-identification.BMC medical informatics and decision making · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present ARX, an anonymization tool that i) implements a wide variety of privacy methods in a highly efficient manner, ii) provides an intuitive cross-platform graphical interface, iii) offers a programming interface for integration into other software systems, and iv) is well documented and actively supported.
Indexed as
Identifiers
25954407PMC4419984What 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.