On the public citation graph

Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation

https://doi.org/10.2196/14064
0 signed · 27 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00521-010-0346-zopenalex-snapshot
Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data
inferredcites →https://doi.org/10.1016/j.ijmedinf.2010.09.007openalex-snapshot
The MITRE Identification Scrubber Toolkit: Design, training, and assessment
inferredcites →https://doi.org/10.1016/j.jbi.2015.06.007openalex-snapshot
Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
inferredcites →https://doi.org/10.1016/j.jbi.2015.06.009openalex-snapshot
Automatic de-identification of electronic medical records using token-level and character-level conditional random fields
inferredcites →https://doi.org/10.1016/j.jbi.2015.07.020openalex-snapshot
Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus
inferredcites →https://doi.org/10.1016/j.jbi.2017.05.023openalex-snapshot
De-identification of clinical notes via recurrent neural network and conditional random field
inferredcites →https://doi.org/10.1093/jamia/ocw156openalex-snapshot
De-identification of patient notes with recurrent neural networks
inferredcites →https://doi.org/10.1093/jamia/ocy017openalex-snapshot
Distributed deep learning networks among institutions for medical imaging
inferredcites →https://doi.org/10.1109/csci.2016.0043openalex-snapshot
A Collaborative Privacy-Preserving Deep Learning System in Distributed Mobile Environment
inferredcites →https://doi.org/10.1109/csci33461.2014openalex-snapshot
2014 International Conference on Computational Science and Computational Intelligence
inferredcites →https://doi.org/10.1109/tnn.2009.2026902openalex-snapshot
Privacy-Preserving Backpropagation Neural Network Learning
inferredcites →https://doi.org/10.1109/tnnls.2016.2582924openalex-snapshot
LSTM: A Search Space Odyssey
inferredcites →https://doi.org/10.1109/tpds.2013.18openalex-snapshot
Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing
inferredcites →https://doi.org/10.1136/amiajnl-2012-001012openalex-snapshot
Large-scale evaluation of automated clinical note de-identification and its impact on information extraction
inferredcites →https://doi.org/10.1145/2810103.2813677openalex-snapshot
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
inferredcites →https://doi.org/10.1145/2810103.2813687openalex-snapshot
Privacy-Preserving Deep Learning
inferredcites →https://doi.org/10.1186/1471-2288-10-70openalex-snapshot
Automatic de-identification of textual documents in the electronic health record: a review of recent research

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/widm.1490openalex-snapshot
Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions
inferred← citeshttps://doi.org/10.1007/978-3-031-39539-0_3openalex-snapshot
Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110239openalex-snapshot
Review learning: Real world validation of privacy preserving continual learning across medical institutions
inferred← citeshttps://doi.org/10.1093/aje/kwae215openalex-snapshot
Invited commentary: deep learning—methods to amplify epidemiologic data collection and analyses
inferred← citeshttps://doi.org/10.1109/access.2020.3027841openalex-snapshot
Applications and Evaluations of Bio-Inspired Approaches in Cloud Security: A Review
inferred← citeshttps://doi.org/10.1109/i2itcon65200.2025.11210548openalex-snapshot
Federated Explainable Mental Health Analytics (FEMHA): A Sustainable Framework for SDG-Aligned Risk Prediction and Emerging Challenges
inferred← citeshttps://doi.org/10.2196/23728openalex-snapshot
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data
inferred← citeshttps://doi.org/10.2196/38154openalex-snapshot
An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation
inferred← citeshttps://doi.org/10.2196/46455openalex-snapshot
Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China
inferred← citeshttps://doi.org/10.3390/app122211709openalex-snapshot
A Survey of Deep Learning for Electronic Health Records
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.