Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach.

TextNetTopics (Yousef et al. in Front Genet 13:893378, 2022. https://doi.org/10.3389/fgene.2022.893378 ) is a recently developed approach that performs text classification-based topics (a topic is a group of terms or words) extracted from a Latent Dirichlet Allocation topic modeling as features rather than individual words. Following this approach enables TextNetTopics to fulfill dimensionality reduction while preserving and embedding more thematic and semantic information into the text document representations. In this article, we introduced a novel approach, the Ensemble Topic Model for Topic Selection (ENTM-TS), an advancement of TextNetTopics. ENTM-TS integrates multiple topic models using the Grouping, Scoring, and Modeling approach, thereby mitigating the performance variability introduced by employing individual topic modeling methods within TextNetTopics. Additionally, we performed a thorough comparative study to evaluate TextNetTopics' performance using eleven state-of-the-art topic modeling algorithms. We used the extracted topics for each as input to the G component in the TextNetTopics tool to select the most compelling topic model regarding their predictive behavior for text classification. We conducted our comprehensive evaluation utilizing the Drug-Induced Liver Injury textual dataset from the CAMDA community and the WOS-5736 dataset. The experimental results show that the Latent Semantic Indexing provides comparable performance measures with fewer discriminative features when compared with other topic modeling methods. Moreover, our evaluation reveals that the performance of ENTM-TS surpasses or aligns with the optimal outcomes obtained from individual topic models across the two datasets, establishing it as a robust and effective enhancement in text classification tasks.

Public Perceptions of Enuresis: Insights From Online Communities in South Korea and the United States.

PURPOSE: To establish a foundation for raising awareness and disseminating accurate information about enuresis-one of the most challenging conditions to discuss openly-this paper examines public perceptions of enuresis. METHODS: This paper collected title and text data from posts related to enuresis on the top popular online platforms such Naver Cafe in South Korea and Reddit in the United States (US). The data along with the thematic subcommunities where the posts were uploaded, was analyzed and visualized using word cloud, Latent Dirichlet Allocation (LDA) topic modeling, and pyLDAvis. RESULTS: The findings reveal both similarities and differences in how the patients from the 2 countries addressed enuresis online. In both countries, enuresis symptoms were a daily concern, and individuals used online platforms as a space to talk about their experiences. However, South Koreans were more inclined to describe symptoms within region-based communities or mothers' forums, where they exchanged information and shared experiences before consulting a doctor. In contrast, US patients with medical experience or knowledge frequently discussed treatment processes, lifestyle adjustments, and medication options. CONCLUSION: South Koreans tend to be cautious when selecting and visiting hospitals, often relying on others for advice and preparation before seeking medical attention. Compared to online communities in the US, Korean users are more likely to seek preliminary diagnoses based on nonprofessional opinions. Consequently, it is important to lower the barriers for patients to access professional medical advice to mitigate the potential harm of relying on nonprofessional opinions. Additionally, there is a need to raise awareness so that adults can recognize and address their symptoms in a timely manner.

Integration of AI-Generated Images in Clinical Otolaryngology.

Recent advances in generative artificial intelligence (AI) have enabled remarkable capabilities in generating images, audio, and videos from textual descriptions. Tools like Midjourney and DALL-E 3 can produce striking visualizations from simple prompts, while services like Kaiber.ai and RunwayML Gen-2 can generate short video clips. These technologies offer intriguing possibilities for clinical and educational applications in otolaryngology. Visualizing symptoms like vertigo or tinnitus could bolster patient-provider understanding, especially for those with communication challenges. One can envision patients selecting images to complement chief complaints, with AI-generated differential diagnoses. However, inaccuracies and biases necessitate caution. Images must serve to enrich, not replace, clinical judgment. While not a substitute for healthcare professionals, text-to-image and text-to-video generation could become valuable complementary diagnostic tools. Harnessed judiciously, generative AI offers new ways to enhance clinical dialogues. However, education on proper, equitable usage is paramount as these rapidly evolving technologies make their way into medicine.

Visual place recognition from end-to-end semantic scene text features.

We live in a visual world where text cues are abundant in urban environments. The premise for our work is for robots to capitalize on these text features for visual place recognition. A new technique is introduced that uses an end-to-end scene text detection and recognition technique to improve robot localization and mapping through Visual Place Recognition (VPR). This technique addresses several challenges such as arbitrary shaped text, illumination variation, and occlusion. The proposed model captures text strings and associated bounding boxes specifically designed for VPR tasks. The primary contribution of this work is the utilization of an end-to-end scene text spotting framework that can effectively capture irregular and occluded text in diverse environments. We conduct experimental evaluations on the Self-Collected TextPlace (SCTP) benchmark dataset, and our approach outperforms state-of-the-art methods in terms of precision and recall, which validates the effectiveness and potential of our proposed approach for VPR.

The FDA Sentinel Real World Evidence Data Enterprise (RWE-DE).

PURPOSE: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. METHODS: In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. RESULTS: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. CONCLUSIONS: Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

Predictors of Participation in a Perinatal Text Message Screening Protocol for Maternal Depression and Anxiety: Prospective Cohort Study.

BACKGROUND: Universal screening for depression and anxiety in pregnancy has been recommended by several leading medical organizations, but the implementation of such screening protocols may overburden health care systems lacking relevant resources. Text message screening may provide a low-cost, accessible alternative to in-person screening assessments. However, it is critical to understand who is likely to participate in text message-based screening protocols before such approaches can be implemented at the population level. OBJECTIVE: This study aimed to examine sources of selection bias in a texting-based screening protocol that assessed symptoms of depression and anxiety across pregnancy and into the postpartum period. METHODS: Participants from the Montreal Antenatal Well-Being Study (n=1130) provided detailed sociodemographic information and completed questionnaires assessing symptoms of depression (Edinburgh Postnatal Depression Scale [EPDS]) and anxiety (State component of the State-Trait Anxiety Inventory [STAI-S]) at baseline between 8 and 20 weeks of gestation (mean 14.5, SD 3.8 weeks of gestation). Brief screening questionnaires, more suitable for delivery via text message, assessing depression (Whooley Questions) and anxiety symptoms (Generalized Anxiety Disorder 2-Item questionnaire) were also collected at baseline and then via text message at 14-day intervals. Two-tailed t tests and Fisher tests were used to identify maternal characteristics that differed between participants who responded to the text message screening questions and those who did not. Hurdle regression models were used to test if individuals with a greater burden of depression and anxiety at baseline responded to fewer text messages across the study period. RESULTS: Participants who responded to the text messages (n=933) were more likely than nonrespondents (n=114) to self-identify as White (587/907, 64.7% vs 39/96, 40.6%; P<.001), report higher educational attainment (postgraduate: 268/909, 29.5% vs 15/94, 16%; P=.005), and report higher income levels (CAD $150,000 [a currency exchange rate of CAD $1=US $0.76 is applicable] or more: 176/832, 21.2% vs 10/84, 11.9%; P<.001). There were no significant differences in symptoms of depression and anxiety between the 2 groups at baseline or postpartum. However, baseline depression (EPDS) or anxiety (STAI-S) symptoms did predict the total number of text message time points answered by participants, corresponding to a decrease of 1% (eß=0.99; P<.001) and 0.3% (eß=0.997; P<.001) in the number of text message time points answered per point increase in EPDS or STAI-S score, respectively. CONCLUSIONS: Findings from this study highlight the feasibility of text message-based screening protocols with high participation rates. However, our findings also highlight how screening and service delivery via digital technology could exacerbate disparities in mental health between certain patient groups.

Feasibility Study of Parkinson's Speech Disorder Evaluation With Pre-Trained Deep Learning Model for Speech-to-Text Analysis.

Objective: This study investigates the feasibility of employing a pre-trained deep learning wave-to-vec model for speech-to-text analysis in individuals with speech disorders arising from Parkinson's disease (PD). Methods: A publicly available dataset containing speech recordings including the Hoehn and Yahr (H&Y) staging, Movement Disorder Society Unified Parkinson's Disease Rating Scale (UPDRS) Part I, UPDRS Part II scores, and gender information from both healthy controls (HC) and those diagnosed with PD was utilized. Employing the Wav2Vec model, a speech-to-text analysis method was implemented on PD patient data. Tasks conducted included word letter classification, word match probability assessment, and analysis of speech waveform characteristics as provided by the model's output. Results: For the dataset comprising 20 cases, among individuals with PD, the H&Y score averaged 2.50±0.67, the UPDRS II-part 5 score averaged 0.70±1.00, and the UPDRS III-part 18 score averaged 0.80±0.98. Additionally, the number of words derived from decoded text subsequent to speech recognition was evaluated, resulting in mean values of 299.10±16.79 and 259.80±93.39 for the HC and PD groups, respectively. Furthermore, the calculated degree of agreement for all syllables was based on the speech process. The accuracy for the reading sentences was observed to be 0.31 and 0.10, respectively. Conclusion: This study aimed to demonstrate the effectiveness of wave-to-vec in enhancing speech-to-text analysis for patients with speech disorders. The findings could pave the way for the development of clinical tools for improved diagnosis, evaluation, and communication support for this population.

Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Background: Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction. Objective: This study aims to enhance the one-to-all (OTA) framework for clinical abbreviation expansion, which uses a single model to predict multiple abbreviation meanings. The objective is to improve OTA by developing context-candidate pairs and optimizing word embeddings in Bidirectional Encoder Representations From Transformers (BERT), evaluating the model's efficacy in expanding clinical abbreviations using real data. Methods: Three datasets were used: Medical Subject Headings Word Sense Disambiguation, University of Minnesota, and Chia-Yi Christian Hospital from Ditmanson Medical Foundation Chia-Yi Christian Hospital. Texts containing polysemous abbreviations were preprocessed and formatted for BERT. The study involved fine-tuning pretrained models, ClinicalBERT and BlueBERT, generating dataset pairs for training and testing based on Huang et al's method. Results: BlueBERT achieved macro- and microaccuracies of 95.41% and 95.16%, respectively, on the Medical Subject Headings Word Sense Disambiguation dataset. It improved macroaccuracy by 0.54%-1.53% compared to two baselines, long short-term memory and deepBioWSD with random embedding. On the University of Minnesota dataset, BlueBERT recorded macro- and microaccuracies of 98.40% and 98.22%, respectively. Against the baselines of Word2Vec + support vector machine and BioWordVec + support vector machine, BlueBERT demonstrated a macroaccuracy improvement of 2.61%-4.13%. Conclusions: This research preliminarily validated the effectiveness of the OTA method for abbreviation disambiguation in medical texts, demonstrating the potential to enhance both clinical staff efficiency and research effectiveness.

Effectiveness of a Digital Health Intervention Leveraging Reinforcement Learning: Results From the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) Randomized Clinical Trial.

BACKGROUND: Digital and mobile health interventions using personalization via reinforcement learning algorithms have the potential to reach large number of people to support physical activity and help manage diabetes and depression in daily life. OBJECTIVE: The Diabetes and Mental Health Adaptive Notification and Tracking Evaluation (DIAMANTE) study tested whether a digital physical activity intervention using personalized text messaging via reinforcement learning algorithms could increase step counts in a diverse, multilingual sample of people with diabetes and depression symptoms. METHODS: From January 2020 to June 2022, participants were recruited from 4 San Francisco, California-based public primary care clinics and through web-based platforms to participate in the 24-week randomized controlled trial. Eligibility criteria included English or Spanish language preference and a documented diagnosis of diabetes and elevated depression symptoms. The trial had 3 arms: a Control group receiving a weekly mood monitoring message, a Random messaging group receiving randomly selected feedback and motivational text messages daily, and an Adaptive messaging group receiving text messages selected by a reinforcement learning algorithm daily. Randomization was performed with a 1:1:1 allocation. The primary outcome, changes in daily step counts, was passively collected via a mobile app. The primary analysis assessed changes in daily step count using a linear mixed-effects model. An a priori subanalysis compared the primary step count outcome within recruitment samples. RESULTS: In total, 168 participants were analyzed, including those with 24% (40/168) Spanish language preference and 37.5% (63/168) from clinic-based recruitment. The results of the linear mixed-effects model indicated that participants in the Adaptive arm cumulatively gained an average of 3.6 steps each day (95% CI 2.45-4.78; P<.001) over the 24-week intervention (average of 608 total steps), whereas both the Control and Random arm participants had significantly decreased rates of change. Postintervention estimates suggest that participants in the Adaptive messaging arm showed a significant step count increase of 19% (606/3197; P<.001), in contrast to 1.6% (59/3698) and 3.9% (136/3480) step count increase in the Random and Control arms, respectively. Intervention effectiveness differences were observed between participants recruited from the San Francisco clinics and those recruited via web-based platforms, with the significant step count trend persisting across both samples for participants in the Adaptive group. CONCLUSIONS: Our study supports the use of reinforcement learning algorithms for personalizing text messaging interventions to increase physical activity in a diverse sample of people with diabetes and depression. It is the first to test this approach in a large, diverse, and multilingual sample. TRIAL REGISTRATION: ClinicalTrials.gov NCT03490253; https://clinicaltrials.gov/study/NCT03490253. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2019-034723.

Characterizing pituitary adenomas in clinical notes: Corpus construction and its application in LLMs.

Objective: Faced with the challenges of differential diagnosis caused by the complex clinical manifestations and high pathological heterogeneity of pituitary adenomas, this study aims to construct a high-quality annotated corpus to characterize pituitary adenomas in clinical notes containing rich diagnosis and treatment information. Methods: A dataset from a pituitary adenomas neurosurgery treatment center of a tertiary first-class hospital in China was retrospectively collected. A semi-automatic corpus construction framework was designed. A total of 2000 documents containing 9430 sentences and 524,232 words were annotated, and the text corpus of pituitary adenomas (TCPA) was constructed and analyzed. Its potential application in large language models (LLMs) was explored through fine-tuning and prompting experiments. Results: TCPA had 4782 medical entities and 28,998 tokens, achieving good quality with the inter-annotator agreement value of 0.862-0.986. The LLMs experiments showed that TCPA can be used to automatically identify clinical information from free texts, and introducing instances with clinical characteristics can effectively reduce the need for training data, thereby reducing labor costs. Conclusion: This study characterized pituitary adenomas in clinical notes, and the proposed method were able to serve as references for relevant research in medical natural language scenarios with highly specialized language structure and terminology.

A Digital Sexual Health Intervention for Urban Adolescent and Young Adult Male Emergency Department Patients: User-Centered Design Approach.

BACKGROUND: Adolescents and young adults frequently present to the emergency department (ED) for medical care and continue to have many unmet sexual health needs. Digital interventions show promise to improve adolescent and young adult sexual health; yet, few interventions focus on male ED patients, despite their infrequent use of contraceptives and rising rates of sexually transmitted infections. OBJECTIVE: This paper describes the design and development of Dr. Eric (Emergency Room Interventions to Improve Care), a digital app focused on promoting condom use among sexually active adolescent and young adult male ED patients. METHODS: This study followed 4 phases of app development, which were based on user-centered design and the software development lifecycle. In phase 1, define, we explored our target population and target health problem (infrequent condom use among male ED patients) by collecting key stakeholder input and conducting in-depth interviews with male patients and urban ED medical providers. In phase 2, discover, we partnered with a digital product agency to explore user experience and digital strategy. In phase 3, design, we refined Dr. Eric's content, a 5-part sexual health educational module and a 10-week SMS text messaging program that focuses on condom use and partner communication about effective contraceptives. We conducted semistructured interviews with male adolescent and young adults to gather feedback on the app and perform usability testing, editing the app after each interview. We also interviewed informatics experts to assess the usability of a high-fidelity prototype. Interviews were recorded and analyzed via descriptive thematic analysis; informatic expert feedback was categorized by Nielsen's heuristic principles. In phase 4, develop, we created the technical architecture and built a responsive web app. These findings were gathered leading to the final version of the digital Dr. Eric program. RESULTS: Using data and key stakeholder input from phases 1 and 2, we iteratively created the Dr. Eric prototype for implementation in the ED setting. Interviews with 8 adolescent and young adult male ED patients suggested that users preferred (1) straightforward information, (2) a clear vision of the purpose of Dr. Eric, (3) open-ended opportunities to explore family planning goals, (4) detailed birth control method information, and (5) games presenting novel information with rewards. Five usability experts provided heuristic feedback aiming to improve the ease of use of the app. These findings led to the final version of Dr. Eric. CONCLUSIONS: Following these mobile health development phases, we created a digital sexual health mobile health intervention incorporating the principles of user experience and interface design. Dr. Eric needs further evaluation to assess its efficacy in increasing condom use among adolescent and young adult male ED patients. Researchers can use this framework to form future digital health ED-based digital interventions.

A comprehensive evaluation of large language models in mining gene relations and pathway knowledge.

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large-scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)-based models and open-source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API-based models GPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged behind their API-based counterparts, whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh-aza).

Text mining approach in chicken meat welfare.

The aim of the study was to analyze the aspects affecting broiler welfare with the use of Text Mining technique. This approach converts text into numerical data and analyzes word frequency distributions, enabling the extraction of useful information and the identification of relationships between elements. Text mining has limitations, i.e. ambiguity and context sensitivity, making it difficult to capture nuanced meanings. The search was carried out with Scopus using the term "Welfare" with the keywords "Chicken", "Broiler", "Broiler chicken", and "Chicken meat", to create a database of abstracts. Text Mining and Topic Analysis were performed on the abstracts (1228 documents) using the Software R 4.3.1., analyzing also the weight of bigram and trigram. Publications on broiler welfare are present in the bibliography since 1990's, but in the last 10 years, for the interest of public opinion, the numbers of publications significantly increased (76.5% of all documents published). USA, Brazil, and Europe-27 published 60% of the documents found. The works were published in a high number of journals, but 37% of them are published in only 4 journals (Poultry Science, Animals, Applied Animal Behavior Science and Animal Welfare). Text Mining analysis identified key terms related to the slaughter phase, housing management, and environmental conditions such as light quality and quantity. Moreover, a high correlation was found between some terms, underlying the importance of the effects of rearing, slaughter phases and litter management on broiler welfare. Most of the countries focused their research on some specific topics identified by Topic Analysis, mainly genetic selection, feeding, stocking density, slaughter, and consumer perceptions. Poultry Science published the highest number of papers (18%) and the topics more investigated were growing performance, transport and slaughter, and litter management. In conclusion, the high number of publications on chicken welfare underlines the importance of broiler welfare both in Europe and in other countries, even if it is difficult to identify common research topics among the geographic areas and the evolution over the time.

BERT4Cache: a bidirectional encoder representations for data prefetching in cache.

Cache plays a crucial role in improving system response time, alleviating server pressure, and achieving load balancing in various aspects of modern information systems. The data prefetch and cache replacement algorithms are significant factors influencing caching performance. Due to the inability to learn user interests and preferences accurately, existing rule-based and data mining caching algorithms fail to capture the unique features of the user access behavior sequence, resulting in low cache hit rates. In this article, we introduce BERT4Cache, an end-to-end bidirectional Transformer model with attention for data prefetch in cache. BERT4Cache enhances cache hit rates and ultimately improves cache performance by predicting the user's imminent future requested objects and prefetching them into the cache. In our thorough experiments, we show that BERT4Cache achieves superior results in hit rates and other metrics compared to generic reactive and advanced proactive caching strategies.

Randomised controlled pilot trial of the EndoSMS supportive text message intervention for individuals with endometriosis: Feasibility and acceptability results.

OBJECTIVE: Diminished quality of life, inadequate support and social isolation are commonly experienced by individuals living with the chronic pain condition, endometriosis. We aimed to determine the feasibility and acceptability of EndoSMS, a psychologically-focused text message intervention designed to support individuals living with endometriosis. METHODS: As part of a two-arm parallel pilot randomised controlled trial with waitlist control, the feasibility and acceptability of a brief (3-month) version of EndoSMS was assessed using a mixed methods approach. Feasibility data (uptake, attrition, text message delivery analytics) and user acceptability (via self-report survey items and written feedback) were assessed. Qualitative data were thematically analysed using the template approach. Primary trial outcomes are not reported in this paper. RESULTS: Feasibility was indicated by: high conversion rate (99.1 %), low attrition (14.2 %), few opt-outs (0.02 %) and a high message delivery rate (99.8 %). Most intervention participants indicated user acceptability (mean = 4.02/5) across self-report questions. Most rated the length of the program (65.5 %), and the number (80.9 %) and language (94.5 %) of the text messages to be 'just right'. Thematic analysis created four themes: A shared "battle": Feeling less isolated and alone; "Be kind to yourself": A focus on self-care, self-compassion and active coping; Keeping endometriosis at the forefront: Helpful or stressful?; Mixed perceptions surrounding the provision of general endometriosis information; and, Tailoring of text messages. CONCLUSION: EndoSMS supportive text message program was feasible and acceptable for individuals with endometriosis. Future developments of the program should consider greater tailoring of content to user needs. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN12621001642875).

Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists.

INTRODUCTION: In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases. METHODS: In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus. RESULTS: Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone. CONCLUSIONS: This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.

Formative Research to Design and Evaluate Caring Text Messages for American Indian and Alaska Native Youth, College Students, and Veterans.

Purpose. Caring Text Messages (CTM) is an evidence-based intervention, developed by the Northwest Portland Area Indian Health Board, modeled after the Caring Contacts (CC) intervention. CC has been shown to prevent suicide deaths, attempts, ideation, and hospitalizations in a variety of settings. Method. Three sets of CTM were developed by American Indian and Alaska Native (AI/AN) teens, college students, and veterans (tailored for each audience), which were reviewed by psychologists familiar with the intervention. To enroll in the service, participants texted a keyword to a text message short code and received two text messages per week with hopeful and encouraging messages. A robust multimedia social marketing campaign was designed to promote the service for each audience. Results. By September 2023, 387 participants enrolled in the Youth CTM intervention, 141 enrolled in the College CTM, and 31 enrolled in the Veterans CTM. Post surveys show elevated levels of user satisfaction. Conclusions. CTM can be tailored to reach populations at higher risk of suicide, including AI/AN youth, college students, and veterans, and connect them to culturally responsive peer and crisis support services. Continued monitoring and evaluation can guide next steps for marketing and outreach and will be useful to determine its impact on those who enroll.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection.

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques-BERT's deep contextual understanding and the generative capabilities of GPT-to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.