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Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning.

JAMA network open
IMPORTANCE: Despite data aggregation and removal of protected health information, there is concern that deidentified physical activity (PA) data collected from wearable devices can be reidentified. Organizations collecting or distributing such data s...

Machine learning in population health: Opportunities and threats.

PLoS medicine
Abraham D. Flaxman and Theo Vos of the Institute for Health Metrics and Evaluation, University of Washington, discuss near-term applications for ML in population health and name their priorities for ongoing ML development.

Machine learning in medicine: Addressing ethical challenges.

PLoS medicine
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians.

Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.

Yearbook of medical informatics
OBJECTIVES:  To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies.

An Artificial Neural Network Framework for Gait-Based Biometrics.

IEEE journal of biomedical and health informatics
As the popularity of wearable and the implantable body sensor network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN device...

A machine learning based approach to identify protected health information in Chinese clinical text.

International journal of medical informatics
BACKGROUND: With the increasing application of electronic health records (EHRs) in the world, protecting private information in clinical text has drawn extensive attention from healthcare providers to researchers. De-identification, the process of id...

Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics.

Journal of the American College of Radiology : JACR
The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these pr...

Big Data in Public Health: Terminology, Machine Learning, and Privacy.

Annual review of public health
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores se...

A cascaded approach for Chinese clinical text de-identification with less annotation effort.

Journal of biomedical informatics
With rapid adoption of Electronic Health Records (EHR) in China, an increasing amount of clinical data has been available to support clinical research. Clinical data secondary use usually requires de-identification of personal information to protect ...

Machine Learning Will Change Medicine.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine