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Background Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. Results We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model’s performance increased compared to the single-task model’s. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. Conclusions Our results show that, on average, the multi-task models produced better NER results than the single-task models trained on a single NER dataset. We also found that Multi-task Learning is beneficial for small datasets. Across the various settings the improvements are significant, demonstrating the benefit of Multi-task Learning for this task.

Background Word representations support a variety of Natural Language Processing (NLP) tasks. The quality of these representations is typically assessed by comparing the distances in the induced vector spaces against human similarity judgements. Whereas comprehensive evaluation resources have recently been developed for the general domain, similar resources for biomedicine currently suffer from the lack of coverage, both in terms of word types included and with respect to the semantic distinctions. Notably, verbs have been excluded, although they are essential for the interpretation of biomedical language. Further, current resources do not discern between semantic similarity and semantic relatedness, although this has been proven as an important predictor of the usefulness of word representations and their performance in downstream applications. Results We present two novel comprehensive resources targeting the evaluation of word representations in biomedicine. These resources, Bio-SimVerb and Bio-SimLex, address the previously mentioned problems, and can be used for evaluations of verb and noun representations respectively. In our experiments, we have computed the Pearson’s correlation between performances on intrinsic and extrinsic tasks using twelve popular state-of-the-art representation models (e.g. word2vec models). The intrinsic–extrinsic correlations using our datasets are notably higher than with previous intrinsic evaluation benchmarks such as UMNSRS and MayoSRS. In addition, when evaluating representation models for their abilities to capture verb and noun semantics individually, we show a considerable variation between performances across all models. Conclusion Bio-SimVerb and Bio-SimLex enable intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimVerb and Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs). These are needed to identify the most accurate methods for learning class-specific representations. Bio-SimVerb and Bio-SimLex are publicly available.

infection causes chronic gastric inflammation that contributes to various gastroduodenal diseases, including peptic ulcer and gastric cancer. Despite broad regional variations, the prevalence of resistance to antibiotics used to manage infection is increasing worldwide; this trend could hinder the success of eradication therapy. To increase awareness of and improve the diagnosis and treatment of its infection in Hong Kong, our consensus panel proposed a set of guidance statements for disease management. We conducted a comprehensive review of literature published during 2011 and 2021, with a focus on articles from Hong Kong or other regions of China. We evaluated the evidence using the Oxford Centre for Evidence-Based Medicine's 2011 Levels of Evidence and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system and sought consensus through online voting and a subsequent face-to-face meeting, which enabled us to develop and refine the guidance statements. This report consists of 24 statements regarding the epidemiology and burden, screening and diagnosis, and treatment of . Key guidance statements include a recommendation to use the test-and-treat approach for high-risk individuals, as well as the confirmation that triple therapy with a proton pump inhibitor, amoxicillin, and clarithromycin remains a valid first-line option for adults and children in Hong Kong.

Introduction: Telemedicine services worldwide have experienced unprecedented growth since the early days of the coronavirus disease 2019 (COVID-19) pandemic. Multiple studies have shown that telemedicine is an effective alternative to conventional in-person patient care. This study explored the public perception of telemedicine in Hong Kong, specifically among older adults who are most vulnerable to COVID-19.Methods: Medical students from The Chinese University of Hong Kong conducted in-person surveys of older adults aged ≥60 years. Each survey collected socio-demographic information, medical history, and concerns regarding telemedicine use. Univariate and multivariate logistic regression analyses were conducted to identify statistically significant associations. The primary outcomes were acceptance of telemedicine use during a hypothetical severe outbreak and after the COVID-19 pandemic.Results: There were 109 survey respondents. Multivariate logistic regression analyses revealed that the expectation of government subsidies for telemedicine services was the strongest common driver and the only positive independent predictor of telemedicine use during a hypothetical severe outbreak (P=0.016) and after the COVID-19 pandemic (P=0.003). No negative independent predictors of telemedicine use during a hypothetical severe outbreak were identified. Negative independent predictors of telemedicine use after the COVID-19 pandemic included older age and residence in the New Territories (both P=0.001).Conclusions: Government support, such as telemedicine-specific subsidies, will be important for efforts to promote telemedicine use in Hong Kong during future severe outbreaks and after the COVID-19 pandemic. Robust dissemination of information regarding the advantages and disadvantages of telemedicine for the public, especially older adults, is needed.

There are sparse data on whether non-pharmaceutical interventions can reduce the spread of influenza. We implemented a study of the feasibility and efficacy of face masks and hand hygiene to reduce influenza transmission among Hong Kong household members. We conducted a cluster randomized controlled trial of households (composed of at least 3 members) where an index subject presented with influenza-like-illness of <48 hours duration. After influenza was confirmed in an index case by the QuickVue Influenza A+B rapid test, the household of the index subject was randomized to 1) control or 2) surgical face masks or 3) hand hygiene. Households were visited within 36 hours, and 3, 6 and 9 days later. Nose and throat swabs were collected from index subjects and all household contacts at each home visit and tested by viral culture. The primary outcome measure was laboratory culture confirmed influenza in a household contact; the secondary outcome was clinically diagnosed influenza (by self-reported symptoms). We randomized 198 households and completed follow up home visits in 128; the index cases in 122 of those households had laboratory-confirmed influenza. There were 21 household contacts with laboratory confirmed influenza corresponding to a secondary attack ratio of 6%. Clinical secondary attack ratios varied from 5% to 18% depending on case definitions. The laboratory-based or clinical secondary attack ratios did not significantly differ across the intervention arms. Adherence to interventions was variable. The secondary attack ratios were lower than anticipated, and lower than reported in other countries, perhaps due to differing patterns of susceptibility, lack of significant antigenic drift in circulating influenza virus strains recently, and/or issues related to the symptomatic recruitment design. Lessons learnt from this pilot have informed changes for the main study in 2008. ClinicalTrials.gov NCT00425893 HKClinicalTrials.com HKCTR-365.

Background VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data. Results Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet. Conclusion This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.

In the context of escalating climate change, it is imperative to understand its multifaceted impacts on financial markets, as climate risks not only affect the low-order moments (mean and variance) but also the high-order moments (skew and kurtosis) of the energy market and the bond market. This study employs a quantile vector autoregressive framework, a combination of time-domain and frequency-domain analyses, and quantile-to-quantile regression to assess the dynamic spillover effects under varying market conditions. The results reveal that spillover effects are particularly pronounced during extreme events, both high positive shocks (above the 80th percentile) or high negative changes (below the 20th percentile). Furthermore, during periods of high climate risks, the dynamic interaction between the energy market and green bonds intensifies, strengthening their roles in the context of spillover effects and altering their respective positions. Our findings also exhibit that the coal markets and green bonds act as net recipients of spillovers, highlighting their potential as effective hedging instruments. Finally, climate risks contribute to an increasing spillover of risk in the new energy sector, with the long-term trend showing the most significant growth in spillover intensity.

Doc number: 30 Abstract Background: This study aimed to examine the prevalence, risk factors, detection rates and management of primary care depression in Hong Kong. Methods: A cross-sectional survey containing the PHQ-9 instrument was conducted on waiting room patients of 59 primary care doctors. Doctors blinded to the PHQ-9 scores reported whether they thought their patients had depression and their management. Results: 10,179 patients completed the survey (response rate 81%). The prevalence of PHQ-9 positive screening was 10.7% (95% CI: 9.7%-11.7%). Using multivariate analysis, risk factors for being PHQ-9 positive included: being female; aged ≤34 years; being unmarried; unemployed, a student or a homemaker; having a monthly household income < HKD $30,000 (USD$ 3,800); being a current smoker; having no regular exercise; consulted a doctor or Chinese medical practitioner within the last month; having ≥ two co-morbidities; having a family history of mental illness; and having a past history of depression or other mental illness. Overall, 23.1% of patients who screened PHQ-9 positive received a diagnosis of depression by the doctor. Predictors for receiving a diagnosis of depression included: having higher PHQ-9 scores; a past history of depression or other mental health problem; being female; aged ≥35 years; being retired or a homemaker; being non-Chinese; having no regular exercise; consulted a doctor within the last month; having a family history of mental health problems; and consulted a doctor in private practice. In patients diagnosed with depression, 43% were prescribed antidepressants, 11% were prescribed benzodiazepines, 42% were provided with counseling and 9% were referred, most commonly to a counselor. Conclusion: About one in ten primary care patients screen positive for depression, of which doctors diagnose depression in approximately one in four. At greatest risk for depression are patients with a past history of depression, who are unemployed, or who have multiple illnesses. Patients most likely to receive a diagnosis of depression by a doctor are those with a past history of depression or who have severe symptoms of depression. Chinese patients are half as likely to be diagnosed with depression as non-Chinese patients. Over half of all patients diagnosed with depression are treated with medications.