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Menampilkan 1–2 dari 2 artikel
Reinforcement Learning for Personalised Critical Care Treatment using Scalable Parallel Computing
Utomo, Chandra Prasetyo
; Ichikawa, Kohei
; Insani, Nashuha
; Thonglek, Kundjanasith
; Xingyuan, Kang
; Maulani, Chaerita
; Rachmawati, Ummi Azizah
Sepsis is one of the leading causes of death in intensive care units. Many patients do not receive timely or effective treatment, which lowers their chances of survival. We developed a reinforcement learning–based framework to provide personalised treatment recommendations for sepsis patients. The model creates simple patient representations from treatment responses, groups patients with similar patterns, and learns the best treatment policy for each group. To reduce long training time, we use p...
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Analysis for Non-covalent Bonds in Protein Structures Using the DV-Xα Method
Halogen atoms are increasingly recognized for their ability to form non-covalent halogen bonds in protein–ligand complexesy. To systematically evaluate these interactions, we employed the DV-Xα method to calculate bond overlap populations (BOPs) in protein structures containing halogenated ligands. Using the Protein Data Bank, we identified thousands of entries with fluorine, chlorine, bromine, or iodine atoms. Structural coordinates were extracted around the chlorine atoms of the ligand and sub...
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