Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability a…
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15 results for “De-identification”
Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objectiv…
Review of automatic de-identification systems for EHR, highlighting challenges beyond accuracy.
problem Challenges in surrogate generation and patient privacy in de-identification of EHR.
method Comprehensive review of 18 recently published systems, focusing on accuracy and challenges.
result Despite accuracy improvements, challenges remain in surrogate generation and patient privacy.
Automatic video modification to hide faces while maintaining pose, illumination, and expression.
problem Face de-identification in video to protect identities.
method A novel feed-forward encoder-decoder network architecture conditioned on facial image high-level representation.
result Fully automatic video modification at high frame rates with minimal distortion.
Proposes k-Same-Siamese-GAN for de-identifying facial images efficiently and preserving privacy.
problem Ensuring privacy of facial images while preserving useful data.
method Uses k-Same-Anonymity, GAN, hyperparameter tuning, and mixed precision training.
result Efficient de-identification of high-resolution facial images with privacy guarantees.
Hybrid AI and rule-based framework de-identifies medical imaging data.
problem De-identifying medical imaging data to protect PHI and PII.
method Combines rule-based and AI techniques with uncertainty quantification.
result Robust performance across benchmark datasets and regulatory standards.
Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: la…
Scrubbing PHI data from medical records is now efficient and scalable with SpaCy.
problem Efficiency and scalability of de-identification techniques for PHI data.
method Evaluated numerous deep learning techniques including SpaCy for performance and efficiency.
result SpaCy model is both well performing and extremely efficient for PHI data scrubbing.
Challenge hides and seeks privacy in clinical time-series data.
problem De-identifying clinical time-series data while preserving utility and privacy.
method Synthetic data generation to preserve temporal dynamics and limit re-identification risk.
result A novel competition tracks synthetic data generation and patient re-identification.
Generates synthetic EHR chief complaints from structured data.
problem Generating unstructured text in EHRs.
method Encoder-decoder deep learning model trained on EHR data.
result Synthetic chief complaints preserve epidemiological information.
PHI Scrubber removes sensitive patient info from medical notes.
problem Protecting patient privacy in electronic health records.
method Deep learning model with de-convolutional neural network and bi-directional LSTM-CNN.
result Eliminates individually identifiable information from physician notes.
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanis…
GANs generate realistic ECG signals for medical research.
problem Privacy concerns in sharing medical data.
method Developed GAN architectures to generate synthetic ECG signals.
result GANs can generate diverse, structurally similar synthetic ECG signals.
Clinical models trained on EHRs degrade in performance over time due to data drift.
problem Model performance degradation over time in clinical settings.
method Accessed year of care for each record in MIMIC, aggregated features into clinical concepts, and tested mitigation strategies.
result State-of-the-art models show significant performance drops when tested on future data compared to historical data.
Paper improves preterm birth prediction using neural networks with noisy labels.
problem Predicting preterm birth from noisy EHR diagnosis codes.
method Developed ALC method to correct label noise in deep learning models.
result Improved prediction performance compared to baseline methods.