Relationship Between Pharmaceutical Knowledge and Probability of Medication Errors Among Nurses: A Cross-sectional Study in the Northwest of Iran in 2020

Background: Patient safety is a major concern for health care professionals. Medication errors have been considered a major indicator of health care quality. The lack of pharmacological knowledge is a cause of medication error among nurses. Objectives: The purpose of this study was to investigate the relationship between pharmacological knowledge and the probability of medical errors in nurses working in Urmia hospitals in 2020. Methods: This cross-sectional study included 490 nurses randomly selected from among those working in hospitals of Urmia in 2020. The data collection tool was a multiple-choice questionnaire about knowledge and pharmacological skills consisting of 3 sections: demographic information, nurses’ drug knowledge, and the confidence level of response in nurses. To analyze questions and hypotheses via SPSS version 21, the t-test and analysis of variance (ANOVA) were employed. Results: The highest pharmaceutical knowledge scores of nurses were related to methods of administration (2.9 ± 1.01 [72.56%]), and the lowest score was related to drug management (1.05 ± 0.63 [52.84%]). The mean of error probability was very low in 28.81% of nurses, low in 37.66%, high in 11.34%, and very high in 22.85%. Pharmaceutical knowledge had a significant relationship with gender, wards, type of hospital, and number of children (P < 0.05 for all). Conclusions: Since the nurses’ level of pharmaceutical knowledge has an important role in the correct prescription of medicine, we suggest that nurse managers and educational supervisors in the field of nursing use in-service training programs and prepare training booklets and posters to promote nurses’ pharmaceutical knowledge in this field.

Drug Knowledge Extraction Framework with Entity Pair Calibration for Chinese Drug Instructions

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 >= 0.95) and their relations (F1 >= 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.

Drug knowledge discovery via multi-task learning and pre-trained models

Background Drug repurposing is to find new indications of approved drugs, which is essential for investigating new uses for approved or investigational drug efficiency. The active gene annotation corpus (named AGAC) is annotated by human experts, which was developed to support knowledge discovery for drug repurposing. The AGAC track of the BioNLP Open Shared Tasks using this corpus is organized by EMNLP-BioNLP 2019, where the “Selective annotation” attribution makes AGAC track more challenging than other traditional sequence labeling tasks. In this work, we show our methods for trigger word detection (Task 1) and its thematic role identification (Task 2) in the AGAC track. As a step forward to drug repurposing research, our work can also be applied to large-scale automatic extraction of medical text knowledge. Methods To meet the challenges of the two tasks, we consider Task 1 as the medical name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. And we regard Task 2 as a relation extraction task, which captures the thematic roles between entities. In this work, we exploit pre-trained biomedical language representation models (e.g., BioBERT) in the information extraction pipeline for mutation-disease knowledge collection from PubMed. Moreover, we design the fine-tuning framework by using a multi-task learning technique and extra features. We further investigate different approaches to consolidate and transfer the knowledge from varying sources and illustrate the performance of our model on the AGAC corpus. Our approach is based on fine-tuned BERT, BioBERT, NCBI BERT, and ClinicalBERT using multi-task learning. Further experiments show the effectiveness of knowledge transformation and the ensemble integration of models of two tasks. We conduct a performance comparison of various algorithms. We also do an ablation study on the development set of Task 1 to examine the effectiveness of each component of our method. Results Compared with competitor methods, our model obtained the highest Precision (0.63), Recall (0.56), and F-score value (0.60) in Task 1, which ranks first place. It outperformed the baseline method provided by the organizers by 0.10 in F-score. The model shared the same encoding layers for the named entity recognition and relation extraction parts. And we obtained a second high F-score (0.25) in Task 2 with a simple but effective framework. Conclusions Experimental results on the benchmark annotation of genes with active mutation-centric function changes corpus show that integrating pre-trained biomedical language representation models (i.e., BERT, NCBI BERT, ClinicalBERT, BioBERT) into a pipe of information extraction methods with multi-task learning can improve the ability to collect mutation-disease knowledge from PubMed.

Edukasi Apoteker Cilik Terkait Pengenalan Obat Kepada Siswa Kelas 5 SD Negeri Mangkura 1 Makassar

Sources of information about drugs are pharmacists, but many people do not know the Pharmacist profession. This is causes problems not only in adults but in children. Lack of knowledge about drugs in children can lead to drug abuse. The purpose of this activity is to educate Young Pharmacists regarding the introduction of drugs and as an embodiment of drug awareness ambassadors from an early age. This activity has been carried out at SD Negeri Mangkura 1 Makassar with 54 participants from 5th-grade elementary school students. The method of implementing the activity begins with educating the pharmacist on delivering the Young Pharmacist material, singing the Young Pharmacist jingle, and continuing with a question and answer session regarding drug knowledge to elementary school students. This Young Pharmacist education activity got positive results for 5th-grade students of SD Negeri Mangkura 1 Makassar which was seen from the increase in student's knowledge about the Pharmacist profession and students were very enthusiastic about giving good choices or bad choices regarding the safety of drug use. In addition, two Young Pharmacist ambassadors were chosen as the embodiment of drug awareness ambassadors from an early age.

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein–protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene–disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene–disease and disease–drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene–disease, and disease–drug networks.

MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events

The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

Producing gender-blind drug knowledge and representations in prison spaces

Purpose This paper aims to examine knowledge production and problem representation with regard to new psychoactive substances (NPS) in Her Majesty’s Chief Inspector of Prisons (HMCIP) annual reports. Design/methodology/approach Seven annual reports published by HMCIP for England and Wales between 2014 and 2020 have been systematically reviewed drawing on thematic analysis. Findings This paper demonstrates how framing in HMCIP annual reports produced a characterisation of NPS in prisons that inadvertently obstructed gender-sensitive knowledge production and problem representation. The framing formalised knowledge silences about spice in women’s prisons. Originality/value HMCIP annual reports monitor drugs in prisons and this affects how these spaces are represented to government and other stakeholders. This paper provides theoretical and practical insights into how gender-blind knowledge is produced by discussing examples of gender-blind drug representations in a specific policy context.

Oncology boot camp: A preparatory curriculum for advanced pharmacy practice experience students

Introduction The primary objective of this study was to evaluate the effectiveness of an autonomous oncology boot camp on Advanced Pharmacy Practice Experience (APPE) student knowledge. Secondary objectives included assessing student perception of the virtual learning experience and overall comfort level with the material. Methods APPE students rotating through our institution between November 2019 and March 2020 were voluntarily enrolled in a 4-hour oncology-focused boot camp, which included five PlayPosit (Denver, CO, USA) interactive video lectures embedded with case-based application questions followed by one comprehensive web-based Quandary (Victoria, BC, Canada) action-maze case. Student learning was measured by a pre- and post-intervention exam. A web survey tool (Qualtrics, Provo, UT, USA) collected student perceptions evaluating their comfort with oncology-specific drug knowledge and APPE rotations tasks. Results Fifty students enrolled in the oncology boot camp, with 100% completing the pre- and post-intervention assessments. Overall, pre-intervention exam scores (mean: 55.4%, SD: 21.8%) improved by 23.2% following the boot camp (mean: 78.6%, SD: 19.2%; p < 0.001). Students performed better on all 10 exam questions, with 6 questions showing a statistically significant improvement (p < 0.05). Forty-five students (90%) completed the perception surveys. Of those, 93% agreed that it effectively reinforced important oncology knowledge, 91% supported the autonomous design, and 82% would recommend the oncology boot camp for future students. Conclusion The boot camp proved to be a beneficial educational tool that enhanced student knowledge and confidence in navigating common oncology concepts. Students valued the ability to independently complete the activities and supported its continuation.