AIMC Topic: Confidentiality

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A critical perspective on guidelines for responsible and trustworthy artificial intelligence.

Medicine, health care, and philosophy Sep 1, 2020
Artificial intelligence (AI) is among the fastest developing areas of advanced technology in medicine. The most important qualia of AI which makes it different from other advanced technology products is its ability to improve its original program and...
Privacy Humans Artificial Intelligence Guidelines as Topic Confidentiality Philosophy, Medical
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Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.

Journal of the American Medical Informatics Association : JAMIA Aug 1, 2020
OBJECTIVE: We introduce fold-stratified cross-validation, a validation methodology that is compatible with privacy-preserving federated learning and that prevents data leakage caused by duplicates of electronic health records (EHRs).
Confidentiality Data Anonymization Computer Security Algorithms Electronic Health Records Humans Machine Learning
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Generating sequential electronic health records using dual adversarial autoencoder.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only fo...
Software Computer Simulation Humans Electronic Health Records Confidentiality Neural Networks, Computer Machine Learning
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Using word embeddings to improve the privacy of clinical notes.

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2020
OBJECTIVE: In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current technique...
Confidentiality Data Anonymization Health Insurance Portability and Accountability Act Health Records, Personal Electronic Health Records United States Natural Language Processing Humans Algorithms
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Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Anesthesia and analgesia Jun 1, 2020
Clinical Competence Decision Support Systems, Clinical United States Electronic Health Records Point-of-Care Systems Confidentiality Data Collection United States Food and Drug Administration Decision Making Europe Workflow Humans Machine Learning Medical Informatics Anesthesia
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The Ethics of Medical AI and the Physician-Patient Relationship.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees Jan 1, 2020
This article considers recent ethical topics relating to medical AI. After a general discussion of recent medical AI innovations, and a more analytic look at related ethical issues such as data privacy, physician dependency on poorly understood AI he...
Humans Artificial Intelligence Physicians Robotics Physician-Patient Relations Informed Consent European Union Ethics, Medical Confidentiality
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AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2020
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but ...
Machine Learning Genomics Computational Biology Sequence Analysis, DNA Humans Confidentiality Privacy
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The machine giveth and the machine taketh away: a parrot attack on clinical text deidentified with hiding in plain sight.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: Clinical corpora can be deidentified using a combination of machine-learned automated taggers and hiding in plain sight (HIPS) resynthesis. The latter replaces detected personally identifiable information (PII) with random surrogates, allo...
Personally Identifiable Information Ambulatory Care Facilities Computer Security Washington Confidentiality Data Anonymization Machine Learning Humans Delivery of Health Care Electronic Health Records
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Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers.

Studies in health technology and informatics Aug 21, 2019
Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learnin...
Machine Learning Electronic Health Records Deep Learning Confidentiality Data Anonymization Humans
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Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.

Journal of the American Medical Informatics Association : JAMIA May 1, 2019
OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risk...
Blockchain Models, Theoretical Algorithms Information Systems Humans Confidentiality Decision Support Systems, Clinical Computer Communication Networks Machine Learning Information Dissemination Genomics Health Information Systems
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