AIMC Topic: Social Networking

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Network context matters: graph convolutional network model over social networks improves the detection of unknown HIV infections among young men who have sex with men.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: HIV infection risk can be estimated based on not only individual features but also social network information. However, there have been insufficient studies using n machine learning methods that can maximize the utility of such information...
HIV Infections Models, Theoretical Male Machine Learning Adolescent Prospective Studies Sexual Partners Humans Young Adult Disease Transmission, Infectious Neural Networks, Computer Adult Social Networking Models, Statistical Homosexuality, Male
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Detecting Chemotherapeutic Skin Adverse Reactions in Social Health Networks Using Deep Learning.

JAMA oncology Apr 1, 2018
This study reports proof-of-principle early detection of chemotherapeutic-associated skin adverse drug reactions from social health networks using a deep learning–based signal generation pipeline to capture how patients describe cutaneous eruptions.
Publications Skin Diseases Deep Learning Bias Information Storage and Retrieval Product Surveillance, Postmarketing Drug-Related Side Effects and Adverse Reactions Data Mining Humans Antineoplastic Agents Social Networking Community Networks
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The 'time machine' reconstructing ancient Venice's social networks.

Nature Jun 14, 2017
History, Medieval History, 20th Century Archives Plague History, 17th Century History, 19th Century History, 15th Century Humans History, 18th Century Handwriting History, 16th Century Robotics Automation Italy Commerce Machine Learning Datasets as Topic Animals Social Networking History, 21st Century Manuscripts as Topic Reading Famous Persons Books Social Change Banking, Personal Democracy Maps as Topic
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Video-based peer feedback through social networking for robotic surgery simulation: a multicenter randomized controlled trial.

Annals of surgery May 1, 2015
OBJECTIVE: To examine the feasibility and outcomes of video-based peer feedback through social networking to facilitate robotic surgical skill acquisition.
Peer Group Social Networking Learning Curve Robotics Video Recording Feedback General Surgery Humans Internship and Residency Clinical Competence Computer Simulation
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