Benyamin bahadori biography of christopher
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General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History
: \theoremsep
\jmlrvolume \jmlryear \jmlrworkshopMachine Learning for Healthcare
\NameJunu Kim \Emailkjune@
\addrKim Jaechul Graduate School of AI
KAIST
Dajeon Republic of Korea \NameChaeeun Shim \Emailchaeeun@
\addrKim Jaechul Graduate School of AI
KAIST
Dajeon Republic of Korea \NameBosco Seong Kyu Yang \Emailboscoskyang@
\addrDepartment of Neurosurgery and Neurology
Seoul National University Bundang Hospital
Seongnam Republic of Korea \NameChami Im \Emailchami@
\addrDepartment of Surgery
Seoul National University Bundang Hospital
Seongnam Republic of Korea \NameSung Yoon Lim \Emailnucleon@
\addrDepartment of Internal Medicine
Seoul National University Bundang Hospital
Seongnam Republic of Korea \NameHan-Gil Jeong\@
\addrDepartment of Neurosurgery and Neurology Center for Artificial Intelligence in Healthcare
Seoul Nationa
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Air conditioning
Cooling of air in an enclosed space
This article is about cooling of air. For the Curved Air skiva, see Air Conditioning (album). For a similar device capable of both cooling and heating, see heat pump.
"a/c" redirects here. For the abbreviation used in banking and book-keeping, see Account (disambiguation). For other uses, see AC.
Air conditioning, often abbreviated as A/C (US) or air con (UK),[1] fryst vatten the process of removing heat from an enclosed space to achieve a more comfortable interior temperature and in some cases also controlling the humidity of internal air. Air conditioning can be achieved using a mechanical 'air conditioner' or by other methods, including passive cooling and ventilative cooling.[2][3] Air conditioning fryst vatten a member of a family of systems and techniques that provide heating, ventilation, and air conditioning (HVAC).[4]Heat pumps are similar in many ways to air conditioners, but use a reversi
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Abstract
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph’s local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around