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Objective: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation., Methods: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria., Results: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93., Significance: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral., Plain Language Summary: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia., (© 2024 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Background: Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited., Objective: This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security., Methods: A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years., Results: In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): "Global production," "Food insecurity and health," "Use of food banks," "Giving to food banks," "Family poverty," "Food relief provision," "Global food insecurity," "Climate change," "Australian food insecurity," and "Human rights." The topic "Giving to food banks," which focused on support and donation, had the highest proportion of positive sentiment, and "Global food insecurity," which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues., Conclusions: In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area., (©Annika Molenaar, Dickson Lukose, Linda Brennan, Eva L Jenkins, Tracy A McCaffrey. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.03.2024.)

ChatGPT has become a prominent tool for fostering personalized and interactive learning with the advancements in AI technology. This study analyzes 212 academic research articles indexed in the Scopus database as of July 2023. It maps the trajectory of educational studies on ChatGPT, identifying primary themes, influential authors, and contributing institutions. By employing bibliometric indicators and network analysis, the study explores collaboration patterns, citation trends, and the evolution of research interests. The findings show the exponential growth of interest in leveraging ChatGPT for educational purposes and provide insights into the specific educational domains and contexts that have garnered the most attention. Furthermore, the study reveals the collaborative dynamics and intellectual foundations shaping the field by examining co-authorship and citation networks. This bibliometric analysis contributes to a comprehensive understanding of the current state of ChatGPT research in education, offering researchers and practitioners valuable insights into evolving trends and potential future directions for this innovative aspect of AI and learning.

Artificial intelligence (AI) tools, notably ChatGPT, are increasingly recognised for their transformative potential in higher education. This study employs a detailed case study approach complemented by a survey, delving into ChatGPT's impact on pedagogical practices, student engagement, and academic performance. It involved 74 undergraduate and postgraduate students enrolled in data analytics courses in Australia. The quantitative analysis highlights ChatGPT's role in providing personalised and on-demand support, which is highly valued among users for its flexibility and responsiveness, meeting a critical demand in educational settings. Notably, the study identifies a medium effect size ([eta squared] = 0.173) in perceived benefits, indicating that ChatGPT accounts for approximately 17.3% of the variance in improved academic outcomes. However, challenges such as ChatGPT's limited understanding of complex queries and the lack of human interactions are primary concerns, with a medium effect size ([eta squared]=0.289) suggesting significant areas for improvement. Furthermore, statistical analyses reveal a clear relationship between the frequency of ChatGPT usage and the perception of its benefits, underscoring the transformative potential for users who have integrated it into their academic practices. Despite these challenges, the differential impact on users versus non-users highlights the potential for ChatGPT to foster more engaging and effective educational practices. The findings advocate for targeted strategies to epitomise ChatGPT's integration into educational settings, emphasising the need for ongoing research and the development of comprehensive guidelines to navigate its complexities and maximise its educational benefits.

A Ground Coupled Heat Pump (GCHP) is a highly energy efficient heating, ventilation, and air conditioning (HVAC) system that utilises the ground as the heat source when heating and as the heat sink when cooling. This paper investigates GCHP systems with horizontal Ground Heat Exchangers (GHEs) in the rural industry, exemplifying the technology for poultry (chicken) sheds in Australia. This investigation aims to provide an Artificial Neural Network (ANN) model that can be used for GCHP design at various locations with different climates. To this extent, a Transient System Simulation Tool (TRNSYS) model for a typical horizontal GHE applied in a rural farm was first verified. Using this model, over 700,000 hourly performance data items were obtained, covering over 80 different yearly loading patterns under three different climate conditions. The simulated performance data was then used to train the ANN. As a result, the trained ANN can predict the performance of GCHP systems with identical (multiple) GHEs even under climatic conditions (and locations) that have not been specifically trained for. Unlike other works, the newly introduced ANN model is accurate even with limited types of input data, with high accuracy (less than 5% error in most cases tested). This ANN model is 100 times computationally faster than TRNSYS simulations and 10,000 times faster than finite element models. [ABSTRACT FROM AUTHOR]

Background: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. Methods: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. Results: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57–36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. Conclusion: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes. [ABSTRACT FROM AUTHOR]

Background: Chat Generative Pre‐trained Transformer (ChatGPT) is a freely available online artificial intelligence (AI) program capable of understanding and generating human‐like language. This study assessed veterinary students' perceptions about ChatGPT in education and practice. It compared perceptions about ChatGPT between students who had completed a critical analysis task and those who had not. Methods: This cross‐sectional study surveyed 498 Doctor of Veterinary Medicine (DVM) students at The University of Sydney, Australia. Second‐year DVM students researched a veterinary pathogen and then completed a critical analysis of ChatGPT (version 3.5) output for the same pathogen. A survey based on the Technology Acceptance Model was then delivered to all DVM students from all years of the programme, collecting data using Likert‐style, categorical and free‐text items. Results: Over 75% of the 100 respondents reported having used ChatGPT. The students found ChatGPT's output relevant and practical for their use but perceived it as inaccurate. They perceived ChatGPT output to be more useful for veterinary students than for pet owners or veterinarians. Those who had completed the critical analysis assignment had a more positive view of ChatGPT's practicality for veterinary students but noted its authoritative tone even when delivering inaccurate information. Over 50% of the students agreed that information about tools such as ChatGPT should be included in the veterinary curriculum. Students agreed that veterinarians should embrace AI but disagreed that AI would eventually replace the need for veterinarians. Conclusions: A critical appraisal of outputs from AI tools such as ChatGPT may help prepare future veterinarians for the effective use of these tools. [ABSTRACT FROM AUTHOR]

Coxiella burnetii, the causative agent of Q fever, is a zoonotic bacteria of global public health significance. The organism has a complex, diverse, and relatively poorly understood animal reservoir but there is increasing evidence that macropods play some part in the epidemiology of Q fever in Australia. The aim of this cross-sectional survey was to estimate the animal- and tissue-level prevalence of coxiellosis amongst eastern grey (Macropus giganteus) and red (Osphranter rufus) kangaroos co-grazing with domestic cattle in a Q fever endemic area in Queensland. Serum, faeces and tissue samples from a range of organs were collected from 50 kangaroos. A total of 537 tissue samples were tested by real-time PCR, of which 99 specimens from 42 kangaroos (84% of animals, 95% confidence interval [CI], 71% to 93%) were positive for the C. burnetii IS1111 gene when tested in duplicate. Twenty of these specimens from 16 kangaroos (32%, 95% CI 20% to 47%) were also positive for the com1 or htpAB genes. Serum antibodies were present in 24 (57%, 95% CI 41% to 72%) of the PCR positive animals. There was no statistically significant difference in PCR positivity between organs and no single sample type consistently identified C. burnetii positive kangaroos. The results from this study identify a high apparent prevalence of C. burnetii amongst macropods in the study area, albeit seemingly with an inconsistent distribution within tissues and in relatively small quantities, often verging on the limits of detection. We recommend Q fever surveillance in macropods should involve a combination of serosurveys and molecular testing to increase chances of detection in a population, noting that a range of tissues would likely need to be sampled to confirm the diagnosis in a suspect positive animal. [ABSTRACT FROM AUTHOR]

Background: The purpose of this meta-analysis was to provide a pooled prevalence estimate of self-reported disordered eating (SRDE) in athletes based on the available literature, and to identify risk factors for their occurrence. Methods: Across ten academic databases, an electronic search was conducted from inception to 7th January 2024. The proportion of athletes scoring at or above predetermined cutoffs on validated self-reporting screening measures was used to identify disordered eating (DE). Subgroup analysis per country, per culture, and per research measure were also conducted. Age, body mass index (BMI), and sex were considered as associated/correlated factors. Results: The mean prevalence of SRDE among 70,957 athletes in 177 studies (132 publications) was 19.23% (17.04%; 21.62%), I2 = 97.4%, τ2 = 0.8990, Cochran's Q p value = 0. Australia had the highest percentage of SRDE athletes with a mean of 57.1% (36.0%-75.8%), while Iceland had the lowest, with a mean of 4.9% (1.2%-17.7%). The SRDE prevalence in Eastern countries was higher than in Western countries with 29.1% versus 18.5%. Anaerobic sports had almost double the prevalence of SRDE 37.9% (27.0%-50.2%) compared to aerobic sports 19.6% (15.2%-25%). Gymnastics sports had the highest SRDE prevalence rate, with 41.5% (30.4%-53.6%) while outdoor sports showed the lowest at 15.4% (11.6%-20.2%). Among various tools used to assess SRDE, the three-factor eating questionnaire yielded the highest SRDE rate 73.0% (60.1%-82.8%). Meta-regression analyses showed that female sex, older age, and higher BMI (all p < 0.01) are associated with higher prevalence rates of SRDE. Conclusion: The outcome of this review suggests that factors specific to the sport affect eating behaviors throughout an athlete's life. As a result, one in five athletes run the risk of developing an eating disorder. Culture-specific and sport-specific diagnostic tools need to be developed and increased attention paid to nutritional deficiencies in athletes. Plain English summary: Disordered eating (DE) refers to eating behaviors that limit food choices, reduce or exaggerate food intake, cause physical discomfort, create a sense of loss of control, or lead to negative emotions like shame or guilt. The DE label does not signify the presence of a diagnosed mental health disorder, but, rather, describes self-reported, regular eating patterns that psychiatrists consider to fall into the at-risk category for an eating disorder. In this review, we performed a search of academic databases to find all relevant studies that measured the frequency of self-reported DE in athletes. We analyzed 177 studies involving over 70,000 athletes in total. In this study, DE was defined as a score above cut-off on validated screening tests for problematic eating behaviors. Our goal was to estimate the prevalence of DE in athletes globally and to determine the factors that increase risk. We found that approximately 1 in 5 athletes (19%) endorsed DE behaviors such as, among others, restrictive dieting, bingeing, and purging. These behaviors were seen most commonly in indoor sports like gymnastics and less commonly in the context of outdoor sports. Rates were highest in female athletes, older athletes, athletes with high body mass index (BMI) scores, and those from Eastern countries and cultures. Australia had the highest rates (over 50%), while rates were lowest in Iceland. We suggest the development of screening tests specifically tailored and applicable to athletes. In addition, we recommend raising public awareness of the health effects of nutrition in sports. [ABSTRACT FROM AUTHOR]

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios. [ABSTRACT FROM AUTHOR]

Introduction: Over 50% of people affected by cancer report unmet support needs. To address unmet information and psychological needs, non-government organisations such as Cancer Councils (Australia) have developed state-based telephone cancer information and support services. Due to competing demands, evidence of the value of these services is needed to ensure that future investment makes the best use of scarce resources. This research aims to determine the costs and broader economic and social value of a telephone support service, to inform future funding and service provision., Methods and Analysis: A codesigned, evaluative social return on investment analysis (SROI) will be conducted to estimate and compare the costs and monetised benefits of Cancer Council Victoria's (CCV) telephone support line, 13 11 20, over 1-year and 3-year benefit periods. Nine studies will empirically estimate the parameters to inform the SROI and calculate the ratio (economic and social value to value invested): step 1 mapping outcomes (in-depth analysis of CCV's 13 11 20 recorded call data; focus groups and interviews); step 2 providing evidence of outcomes (comparative survey of people affected by cancer who do and do not call CCV's 13 11 20; general public survey); step 3 valuing the outcomes (financial proxies, value games); step 4 establishing the impact (Delphi); step 5 calculating the net benefit and step 6 service improvement (discrete choice experiment (DCE), 'what if' analysis). Qualitative (focus groups, interviews) and quantitative studies (natural language processing, cross-sectional studies, Delphi) and economic techniques (willingness-to-pay, financial proxies, value games, DCE) will be applied., Ethics and Dissemination: Ethics approval for each of the studies will be sought independently as the project progresses. So far, ethics approval has been granted for the first two studies. As each study analysis is completed, results will be disseminated through presentation, conferences, publications and reports to the partner organisations., Competing Interests: Competing interests: NM leads the Economics of Cancer Collaboration between Deakin University and CCV and she holds an honorary position with CCV. JB holds an honorary position with CCV. TH is the Chief Executive Officer, CCV. DSS is the Head of Strategy & Support, CCV. KL was the Head of Cancer Information Support Services, CCV during Steps 1 and 2. SA was CEO of Cancer Council Australia from 2015 to 2020 and currently chairs the Advisory Committee to the Victoria Cancer Registry. All other authors declare that they have no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Electronic Heath Records (EHRs) play vital roles in facilitating streamlined service provision and governance across the Australian health system. Given the recent challenges due to the COVID-19 pandemic, an ageing population, health workforce silos, and growing inefficiencies in traditional systems, a detailed historical analysis of the use of EHR research in Australia is necessary. The aim of this study is to examine the trends and patterns in EHR research in Australia over the past three decades by employing bibliometric methods. A total of 951 articles published in 443 sources were included in the bibliometric analysis. The annual growth rate of EHR research in Australia was about 17.1%. Since 2022, the main trending topics in EHR research were COVID-19, opioid usage, and natural language processing. A thematic analysis indicated aged care, clinical decision support systems, cardiovascular disease, drug allergy, and adverse drug reaction as the "hot" themes in EHR research in Australia. This study reveals a significant uptrend in EHR research in Australia, highlighting the evolving intellectual and collaborative landscape of this interdisciplinary field. The data also provide guidance for policymakers and funding institutions in terms of the most significant contributions and key fields of research while also holding public interest.