Current practices in organization of anesthesia drug tray
Asian Journal of Medical Sciences, 2022 Background: The risk of medication error is high in the operating room, since the anesthesiologist prepares, stores, and administers the medication. Poor labeling practices and cluttered drug trays increase the risk of syringe swap and medication error ...
Sowmya Jois +4 more
doaj +1 more source
Knowledge-Driven New Drug Recommendation [PDF]
arXiv, 2022 Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs with sufficient prescription data from many patients.
arxiv
Self-supervised Learning for Label Sparsity in Computational Drug Repositioning [PDF]
arXiv, 2022 The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of validated drug-disease associations is scarce compared to the number of drugs and diseases in the real world.
arxiv
Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach [PDF]
, 2021 Social media such as Instagram and Twitter have become important platforms for marketing and selling illicit drugs. Detection of online illicit drug trafficking has become critical to combat the online trade of illicit drugs. However, the legal status often varies spatially and temporally; even for the same drug, federal and state legislation can have ...
arxiv +1 more source
Communication of survival data in US Food and Drug Administration-approved labeling of cancer drugs [PDF]
, 2021 This cross-sectional study examines how information on overall survival benefits of novel cancer drug indications is communicated in ...
Guan, Xiaodong +4 more
core +1 more source
Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening [PDF]
arXiv, 2023 Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug ...
arxiv
NRBdMF: A recommendation algorithm for predicting drug effects considering directionality [PDF]
, 2022 Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such ...
arxiv +1 more source
SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19 [PDF]
, 2020 The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study,
arxiv +1 more source
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions [PDF]
arXiv, 2021 Predicting drug-drug interactions (DDI) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e. side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with
arxiv