PURPOSE OF REVIEW: Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both s...
Diagnosis and treatment planning are the most important steps in the orthognathic surgery for the successful treatment. The purpose of this study was to develop a new artificial intelligent model for surgery/non-surgery decision and extraction determ...
INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis ...
Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoi...
Academic medicine : journal of the Association of American Medical Colleges
Oct 1, 2019
The technology at the heart of the most innovative progress in health care artificial intelligence (AI) is in a subdomain called machine learning (ML), which describes the use of software algorithms to identify patterns in very large datasets. ML has...
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article...
Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large...
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorit...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.