The disclosure of diagnosis codes can breach research participants' privacy
By Grigorios Loukides, Joshua Charles Denny, Bradley A. Malin. 2010
143 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Public Health, Environmental and Occupational Health
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimeng Sun
- Edward Choi
- Walter F. Stewart
- Li Huang
- Dianbo Liu
- Carl A. Gunter
- Jon D. Duke
- Vincent Bindschaedler
In Public Health, Environmental and Occupational Health
Citations in the corpus
- Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, neutral): “[34] show that it is possible to identify a wide range of sensitive patient information from de-identified clinical data presented in a form standard among medical researchers, and later proposed a domain-specific utilitypreserving scheme similar to k-anonymity for mitigating these breaches [35].”
- Generating Multi-label Discrete Patient Records using Generative Adversarial Networks (2017, background): “, 2011a), diagnoses (Loukides et al., 2010), lab tests (Atreya et al.”
- Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (2019, background): “This may impede adoption of machine learning on EMRs in reality, and has entailed researchers to raise concerns on central storage of EMRs and on security, cost-effectiveness, privacy and availability of medical data sharing [14-24].”
- Towards Measuring Membership Privacy (2017, background): “Works on re-identification attacks [25, 30, 40] have demonstrated the privacy risks of releasing anonymized datasets.”
- Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks (2017, background): “…be accomplished through residual distinguishable patterns in various features (e.g., demographics (Sweeney, 1997; El Emam et al., 2011a), diagnoses (Loukides et al., 2010), lab tests (Atreya et al., 2013), visits across healthcare providers (Malin and Sweeney, 2004), and genomic variants (Erlich…”