Results 121 to 130 of about 18,276 (179)

Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify postoperative acute gastrointestinal injury. [PDF]

Front Cardiovasc Med
Ma Y, Yang X, Weng C, Wang X, Zhang B, Liu Y, Wang R, Bao Z, Yang P, Zhang H, Liu Y.   +10 more
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Metformin use and the risk of incident immune-mediated diseases in patients with type 2 diabetes: a population-based cohort study. [PDF]

Front Immunol
Zhang Q, Yen FS, Xu C, Lin CL, Yip HT, Hwu CM, Wei JC, Hsu CC.   +7 more
europepmc   +1 more source

Epidemiology and Outcomes of Pediatric Fever in a Rural District of Southern Mozambique: 17 Years of Morbidity Surveillance. [PDF]

Open Forum Infect Dis
Torres-Fernandez D, Dalsuco J, Garcia-Mauriño C, Balanza N, Valente M, Ajanovic S, Varo R, Fanjul J, Bramugy J, Sitoe A, Quintó L, Nhampossa T, Taylor E, Vialard F, Nhacolo A, Baro B, Cossa A, Boca Z, Massora S, Alemany A, Mandomando I, Millat-Martinez P, Aide P, Bassat Q.   +23 more
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Health is beyond genetics: on the integration of lifestyle and environment in real-time for hyper-personalized medicine. [PDF]

Front Public Health
Tan MJT, Kasireddy HR, Satriya AB, Abdul Karim H, AlDahoul N.   +4 more
europepmc   +1 more source

Constructing the early enteral nutrition management protocol for severely burned adult patients: a Delphi study. [PDF]

BMC Nutr
Yang X, Zou X, Chen J, Li W, Wang J, Fan Y, Wen Q, Xu X, Mao X, Li R.   +9 more
europepmc   +1 more source

Prognostic and immunological role of RHEBL1 in pan-cancer: a target for survival and immunotherapy. [PDF]

Discov Oncol
Chen Y, Feng X, Yin X, Yi N, Zhao Y, Wang Q, Xing W, Ma C, Chen D.   +8 more
europepmc   +1 more source

Artificial superintelligence alignment in healthcare. [PDF]

Jpn J Radiol
Ueda D, Walston SL, Kurokawa R, Saida T, Honda M, Iima M, Watabe T, Yanagawa M, Nishioka K, Sofue K, Sakata A, Sugawara S, Kawamura M, Ito R, Takumi K, Oda S, Hirata K, Ide S, Naganawa S.   +18 more
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ETHICAL CONSIDERATIONS IN MACHINE LEARNING AND ARTIFICIAL GENERAL INTELLIGENCE (AGI) MODELS

2022
The ethical landscape of machine learning and AGI is fraught with complex challenges that require careful navigation. Bias in algorithms can perpetuate and amplify existing societal inequalities, leading to unfair treatment in areas such as criminal justice and employment.
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Artificial General Intelligent Terrorism: Ethical AGI

ABSTRACT: This paper explores the ethical challenges and potential risks associated with Artificial General Intelligence (AGI) in the context of terrorism, termed Artificial General Intelligent Terrorism (AGIT). The discussion highlights the urgent need for a collective discourse and regulatory frameworks to address the ethical implications of AGI's ...
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