Health information and resources in hospital outpatient waiting areas may not meet the needs of older adults from culturally and linguistically diverse backgrounds: A cross-cultural qualitative study.

BACKGROUND: Health information and resources are often provided in hospital outpatient waiting areas but may not meet the cultural and health literacy needs of older adults from culturally and linguistically diverse (CALD) backgrounds. OBJECTIVES: To explore the perspectives and experiences of Cantonese- and Vietnamese-speaking patients and carers in this setting. METHODS: This qualitative interview-based study was conducted from December 2019 to March 2020 at a single outpatient rehabilitation service located at a tertiary public hospital. Four adult consumers (two older adult patients, two caregivers) from CALD backgrounds participated in semi-structured interviews with bilingual researchers. Data were transcribed, translated and analysed using reflexive thematic analysis. RESULTS: Five themes were developed which highlighted that older adults' language profiles shaped their health information needs and ability to access resources in waiting areas. Cultural factors such as filial responsibility may also influence health information preferences. DISCUSSION: Older consumers from CALD backgrounds did not have equitable access to health information and resources in the waiting area compared with English-literate older adults. CONCLUSION: Health information and resources in waiting areas warrant improving to better meet the needs of older patients from CALD backgrounds and their caregivers.

Syntactically-informed word representations from graph neural network.

Most deep language understanding models depend only on word representations, which are mainly based on language modelling derived from a large amount of raw text. These models encode distributional knowledge without considering syntactic structural information, although several studies have shown benefits of including such information. Therefore, we propose new syntactically-informed word representations (SIWRs), which allow us to enrich the pre-trained word representations with syntactic information without training language models from scratch. To obtain SIWRs, a graph-based neural model is built on top of either static or contextualised word representations such as GloVe, ELMo and BERT. The model is first pre-trained with only a relatively modest amount of task-independent data that are automatically annotated using existing syntactic tools. SIWRs are then obtained by applying the model to downstream task data and extracting the intermediate word representations. We finally replace word representations in downstream models with SIWRs for applications. We evaluate SIWRs on three information extraction tasks, namely nested named entity recognition (NER), binary and n-ary relation extractions (REs). The results demonstrate that our SIWRs yield performance gains over the base representations in these NLP tasks with 3-9% relative error reduction. Our SIWRs also perform better than fine-tuning BERT in binary RE. We also conduct extensive experiments to analyse the proposed method.

DeepEventMine: end-to-end neural nested event extraction from biomedical texts.

MOTIVATION: Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools. RESULTS: We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance. AVAILABILITY AND IMPLEMENTATION: Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of drying on physical properties, phenolic compounds and antioxidant capacity of Robusta wet coffee pulp (Coffea canephora).

Wet coffee pulp (WCP), produced as waste from coffee production, is a rich source of bioactive compounds, especially caffeine and chlorogenic acid. However, it contains high moisture content, thus it is challenging for further utilization due to degradation and microbial deterioration. Dehydration is, therefore, necessary to minimize degradation and ease storage and transportation. As a waste, the common drying methods should be prioritized to be feasible for industrial application. This study aimed to determine the impact of different drying conditions of the three common drying methods including low temperature and pressure, vacuum and hot air drying on physical, phytochemical and antioxidant properties of WCP to identify the most suitable drying conditions. Browning index, moisture content, total phenolic content (TPC), flavonoids (TFC), proanthocyanidins, and chlorogenic acid as well as the antioxidant properties of the dried coffee pulp were significantly influenced by different tested conditions. Vacuum drying was found to be more suitable for drying the wet coffee pulp as compared to low temperature and pressure and hot air drying methods. Vacuum drying at 110 °C retained the highest TPC (14.4 mg gallic acid equivalents (GAE)/g dry weight (DW)), proanthocyanidins (6.8 mg catechin equivalents (CE)/g DW), TFC (13.2 CE/g DW), caffeine (2.9 mg/g DW) and antioxidant capacity. Chlorogenic acid (3.4 mg/g DW) was 13% lower, but energy consumption was 37% less than vacuum drying at 90 °C. Therefore, vacuum drying (3.75 mmHg) at 110 °C for 4h 05 min was suggested for dehydration of the wet coffee pulp for subsequent recovery and processing.

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.

OBJECTIVE: Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records. MATERIALS AND METHODS: We proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined them using an ensemble method. The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences. RESULTS: Our team ranked third with a micro-averaged F1 score of 94.72% and 87.65% for relation and end-to-end relation extraction, respectively (Tracks 2 and 3). Our ensemble effectively takes advantages from our proposed models. Analysis of the reported results indicated that our proposed approach is more generalizable than the top-performing system, which employs additional training data- and corpus-driven processing techniques. CONCLUSIONS: We proposed a relation extraction system to identify relations between drugs and medication-related entities. The proposed approach is independent of external syntactic tools. Analysis showed that by using latent Drug-Drug interactions we were able to significantly improve the performance of non-Drug-Drug pairs in EHRs.

The economic impact of asthma treatment on patients, 2014-2017: The Vietnamese experience from a public hospital.

OBJECTIVES: To estimate the economic burden of asthma treatment by quantifying direct medical expenditures at one of Southern Vietnam's public hospitals base on the hospital perspective. METHODS: A retrospective, prevalence-based, cost-of-illness analysis was developed using the hospital's electronic medical record data to calculate the economic burden of asthma (ICD-10 code J45, J46) through direct medical costs from January 2014 to December 2017. All costs were converted to US dollars and to the year 2018. Data were analyzed using descriptive statistics. The potential correlations between variables were evaluated using the chisquare test and bootstrap difference. RESULTS: The average direct medical cost of asthma was estimated to range from $34.7 to $55.3 per - outpatient and $45.1 to $107.2 per - inpatient annually. The total economic burdens for 4 years from 2014 to 2017 were $110,387.4 from outpatients and $13,413.8 from inpatients. The most influential component was medication cost. CONCLUSIONS: Asthma places a high economic burden on individuals and the healthcare system in Vietnam. The findings of this study provide health administrators with important evidence to enhance surveillance of the disease and to allow suitable drafting of national health policy.

Lower respiratory tract viral infections in pediatric abdominal organ transplant recipients: a single hospital inpatient cohort study.

Respiratory viral infections are a major cause of morbidity and mortality in solid organ transplant recipients. Early detection of a viral etiology of a LRTI in a febrile transplant recipient can theoretically reduce the use of antibiotics, trigger modification of immunosuppression and prompt appropriate isolation procedures to reduce nosocomial infections. We retrospectively evaluated pediatric abdominal organ transplant recipients hospitalized with respiratory illnesses to determine the viral pathogens identified by various methods including multiplex RT-PCR performed on nasopharyngeal or endotracheal aspirates. Among 30 symptomatic subjects (median age, 2.5 yr) evaluated using this methodology, 25 (83%) were positive for at least one virus. Rhinovirus was the most frequently identified virus (14 subjects). RSV was identified in five subjects with associated mortality of 40%. Parainfluenza, influenza, metapneumovirus, and adenovirus were also identified. This study indicates that rhinovirus is a significant cause of morbidity in this single center cohort of pediatric abdominal organ transplant recipients.

Nonsurgical care of intestinal and multivisceral transplant recipients: a review for the intensivist.

Intestinal and multivisceral transplantation has evolved from an experimental procedure to the treatment of choice for patients with irreversible intestinal failure and serious complications related to long-term parenteral nutrition. Increased numbers of transplant recipients and improved survival rates have led to an increased prevalence of this patient population in intensive care units. Management of intestinal and multivisceral transplant recipients is uniquely challenging because of complications arising from the high incidence of transplant rejection and its treatment. Long-term comorbidities, such as diabetes, hypertension, chronic kidney failure, and neurological sequelae, also develop in this patient population as survival improves. This article is intended for intensivists who provide care to critically ill recipients of intestinal and multivisceral transplants. As perioperative care of intestinal/multivisceral transplant recipients has been described elsewhere, this review focuses on common nonsurgical complications with which one should be familiar in order to provide optimal care. The article is both a review of the current literature on multivisceral and isolated intestinal transplantation as well as a reflection of our own experience at the University of Miami.