相关论文: Scrubbing Sensitive PHI Data from Medical Records …
Confidentiality of patient information is an essential part of Electronic Health Record System. Patient information, if exposed, can cause a serious damage to the privacy of individuals receiving healthcare. Hence it is important to remove…
In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance…
Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural…
De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHRs) to be shared for research. Automatic de-identification classifierscan…
In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant…
Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. Facilitating data sharing and harmonization for multi-center collaborations are…
Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and…
Objective: To enhance automated de-identification of radiology reports by scaling transformer-based models through extensive training datasets and benchmarking performance against commercial cloud vendor systems for protected health…
Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. However, many methods currently used for deidentification have negative effects on downstream…
De-identification is the process of removing 18 protected health information (PHI) from clinical notes in order for the text to be considered not individually identifiable. Recent advances in natural language processing (NLP) has allowed…
Medical health records and clinical summaries contain a vast amount of important information in textual form that can help advancing research on treatments, drugs and public health. However, the majority of these information is not shared…
Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method…
The advent of cost effective cloud computing over the past decade and ever-growing accumulation of high-fidelity clinical data in a modern hospital setting is leading to new opportunities for translational medicine. Machine learning is…
Hyperspectral imaging (HSI) is an advanced medical imaging modality that captures optical data across a broad spectral range, providing novel insights into the biochemical composition of tissues. HSI may enable precise differentiation…
Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the field, however, at…
Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can…
Curating, processing, and combining large-scale medical imaging datasets from national studies is a non-trivial task due to the intense computation and data throughput required, variability of acquired data, and associated financial…
Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is…
Deep learning has driven significant advances in medical image analysis, yet its adoption in clinical practice remains constrained by the large size and lack of transparency in modern models. Advances in interpretability techniques such as…
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…