Social media data is rapidly evolving and accessible which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, 36 studies were selected from 17 countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014-2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified including Dietary patterns, Cooking and recipes, Diet and Health, Public Health and Nutrition and Food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33.33% (open-source engine) to 98.53% (engine developed for the study). The average proportion of sentiment was 38.8% positive, 46.6% neutral and 28.0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.

The COVID‐19 pandemic has disrupted seeking and delivery of healthcare. Different Australian jurisdictions implemented different COVID‐19 restrictions. We used Australian national pharmacy dispensing data to conduct interrupted time series analyses to examine the incidence and prevalence of opioid dispensing in different jurisdictions. Following nationwide COVID‐19 restrictions, the incidence dropped by −0.40 (95% confidence interval [CI]: −0.50, −0.31), −0.33 (95% CI: −0.46, −0.21) and −0.21 (95% CI: −0.37, −0.04) per 1000 people per week and the prevalence dropped by −0.85 (95% CI: −1.39, −0.31), −0.54 (95% CI: −1.01, −0.07) and −0.62 (95% CI: −0.99, −0.25) per 1000 people per week in Victoria, New South Wales and other jurisdictions, respectively. Incidence and prevalence increased by 0.29 (95% CI: 0.13, 0.44) and 0.72 (95% CI: 0.11, 1.33) per 1000 people per week, respectively in Victoria post‐lockdown; no significant changes were observed in other jurisdictions. No significant changes were observed in the initiation of long‐term opioid use in any jurisdictions. More stringent restrictions coincided with more pronounced reductions in overall opioid initiation, but initiation of long‐term opioid use did not change.

No abstract

Avoidable and unplanned readmissions to hospital wards, especially the Intensive Care Unit, have significant implications for the patients’ health and poses additional economic burdens on the health system. If patients who are at risk of readmission are identified early and their risks are mitigated, these complications can be avoided. Machine Learning has been a valuable tool for automatic identification and prediction of various health conditions and situations, including unplanned readmissions. This is made possible through processing large collections of clinical data to build predictive models. However, the clinical data from which these models are built is highly confidential, which has restricted the ability of researchers to provide their data to the wider community, hence limiting reproducibility and comparability between results. The MIMIC databases are large, publicly available clinical datasets, which make reproducibility and comparability feasible. To maximise the benefit the research community derives from this invaluable resource, we developed MiPy, an open source standardised framework for preparing, building, and evaluating machine learning models for predicting both hospital and ICU readmission, on the MIMIC-IV database. The primary aim of this work is to enhance reproducibility and comparability of research in the field.