Sensitive Patient

Sensitive patient data management in healthcare is a critical area focusing on balancing the need for data-driven advancements in medicine with robust privacy protections. Current research emphasizes developing and evaluating methods like federated learning, differential privacy, and homomorphic encryption to enable collaborative data analysis without compromising individual identities. These techniques, often implemented with transformer models and other deep learning architectures, are crucial for advancing AI in healthcare while adhering to ethical and legal standards. The ultimate goal is to facilitate medical research, improve diagnostics and treatment, and build trust in data-driven healthcare systems.

Papers

December 15, 2023

A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease
Paul K. Mandal
Data Detection Multimodal Data Vertical Federated Learning Medical Data Sensitive Patient

November 11, 2023

Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
Medical Image Faulty Negative Mitigation Privacy Risk Sensitive Patient

October 14, 2023

Multimodal Federated Learning in Healthcare: a Review
Jacob Thrasher, Alina Devkota, Prasiddha Siwakotai, Rohit Chivukula, Pranav Poudel, Chaunbo Hu, Binod Bhattarai, Prashnna Gyawali
Federated Learning Narrative Review Healthcare System Medical Domain Sensitive Patient Patient Privacy

June 29, 2023

Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective
Youyang Qu, Lichuan Ma, Wenjie Ye, Xuemeng Zhai, Shui Yu, Yunfeng Li, David Smith
Differential Privacy Edge Intelligence Sensitive Patient Smart Healthcare Linkage Attack Blockchain Based

May 14, 2023

On enhancing the robustness of Vision Transformers: Defensive Diffusion
Raza Imam, Muhammad Huzaifa, Mohammed El-Amine Azz
Adversarial Attack Vision Transformer Native Robustness Adversarial Noise Sensitive Patient

April 30, 2023

Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records
Kai Zhang, Xiaoqian Jiang
Machine Learning Sensitive Data Sensitive Patient Information Detection

April 16, 2023

FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain
Nazar Waheed, Ateeq Ur Rehman, Anushka Nehra, Mahnoor Farooq, Nargis Tariq, Mian Ahmad Jan, Fazlullah Khan, Abeer Z. Alalmaie, Priyadarsi Nanda
Internet of Thing Blockchain Based Platform Security Related Sensitive Patient

March 24, 2023

Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching
Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu
Large Language Model Data Augmentation Clinical Trial Single Example Sensitive Patient Patient Trial Matching

September 1, 2022

Which anonymization technique is best for which NLP task? -- It depends. A Systematic Study on Clinical Text Processing
Iyadh Ben Cheikh Larbi, Aljoscha Burchardt, Roland Roller
Natural Language Processing Task Clinical Text Sensitive Patient Systematic Study Anonymization Technique

August 23, 2022

Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals
Daniel Mauricio Jimenez Gutierrez, Hafiz Muuhammad Hassan, Lorella Landi, Andrea Vitaletti, Ioannis Chatzigiannakis
Application Proficiency Barzilai Borwein Technique Centralized Learning Sensitive Patient 12 Lead

May 30, 2022

FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, Panagiotis Papapetrou
Centralized Machine Learning Sensitive Patient Intensive Care Unit Mortality

May 20, 2022

Unintended memorisation of unique features in neural networks
John Hartley, Sotirios A. Tsaftaris
Neural Network Convolutional Neural Network Privacy Risk Sensitive Patient Memorization Effect Unintended Memorization Unique Feature

March 25, 2022

A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data
Christopher Meaney, Wali Hakimpour, Sumeet Kalia, Rahim Moineddin
Transformer Model Comparative Evaluation Clinical Data Sensitive Patient De Identification

Sensitive Patient

Papers

A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease

Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

Multimodal Federated Learning in Healthcare: a Review

Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective

On enhancing the robustness of Vision Transformers: Defensive Diffusion

Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records

FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain

Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching

Which anonymization technique is best for which NLP task? -- It depends. A Systematic Study on Clinical Text Processing

Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals

FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

Unintended memorisation of unique features in neural networks

A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data