BACKGROUND: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent\'s gender.
OBJECTIVE: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide.
METHODS: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning-based large language model.
RESULTS: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause.
CONCLUSIONS: Linguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities.

UNASSIGNED: Large language model (LLM)-powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate \"human-like\" features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.

The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new era where complex biological data can be made more accessible and interpretable. ChatGPT is a natural language processing tool that can process, interpret, and summarize vast data sets. It can serve as a digital assistant for physicians and researchers, aiding in integrating medical imaging data with other multiomics data and facilitating the understanding of complex biological systems. The physician\'s and AI\'s viewpoints emphasize the value of such AI models in medicine, providing tangible examples of how this could enhance patient care. The editorial also discusses the rise of generative AI, highlighting its substantial impact in democratizing AI applications for modern medicine. While AI may not supersede health care professionals, practitioners incorporating AI into their practices could potentially have a competitive edge.

BACKGROUND: Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement.
OBJECTIVE: This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors\' and residents\' ratings, and specific question types.
METHODS: A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications.
RESULTS: Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5\'s accuracy, beneficial, and completeness dimensions.
CONCLUSIONS: ChatGPT\'s potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.

Psoriasis is an immune-mediated skin disease affecting approximately 3% of the global population. Proper management of this condition necessitates the assessment of the Body Surface Area (BSA) and the involvement of nails and joints. Recently, the integration of Natural Language Processing (NLP) with Electronic Medical Records (EMRs) has shown promise in advancing disease classification and research. This study evaluates the performance of ChatGPT-4, a commercial AI platform, in analyzing unstructured EMR data of psoriasis patients, particularly in identifying affected body areas.

BACKGROUND: Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations.
OBJECTIVE: This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms.
METHODS: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization\'s (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends.
RESULTS: We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines.
CONCLUSIONS: Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

The mechanism that maintains ER-to-Golgi vesicles formation and transport is complicated. As one of the adapters, Ninein-like protein (Nlp) participated in assembly and transporting of partial ER-to-Golgi vesicles that contained specific proteins, such as β-Catenin and STING. Nlp acted as a platform to sustain the specificity and continuity of cargoes during COPII and COPI-coated vesicle transition and transportation through binding directly with SEC31A as well as Rab1B. Thus, we proposed an integrated transport model that particular adapter participated in specific cargo selection or transportation through cooperating with different membrane associated proteins to ensure the continuity of cargo trafficking. Deficiency of Nlp led to vesicle budding failure and accumulation of unprocessed proteins in ER, which further caused ER stress as well as Golgi fragmentation, and PERK-eIF2α pathway of UPR was activated to reduce the synthesis of universal proteins. In contrast, upregulation of Nlp resulted in Golgi fragmentation, which enhanced the cargo transport efficiency between ER and Golgi. Moreover, Nlp deficient mice were prone to spontaneous B cell lymphoma, since the developments and functions of lymphocytes significantly depended on secretory proteins through ER-to-Golgi vesicle trafficking, including IL-13, IL-17 and IL-21. Thus, perturbations of Nlp altered ER-to-Golgi communication and cellular homeostasis, and might contribute to the pathogenesis of B cell lymphoma.

Evidence-informed decision making is based on the premise that the entirety of information on a topic is collected and analyzed. Systematic reviews allow for data from different studies to be rigorously assessed according to PICO principles (population, intervention, control, outcomes). However, conducting a systematic review is generally a slow process that is a significant drain on resources. The fundamental problem is that the current approach to creating a systematic review cannot scale to meet the challenges resulting from the massive body of unstructured evidence. For this reason, the Public Health Agency of Canada has been examining the automation of different stages of evidence synthesis to increase efficiencies. In this article, we present an overview of an initial version of a novel machine learning-based system that is powered by recent advances in natural language processing (NLP), such as BioBERT, with further optimizations completed using a new immunization-specific document database. The resulting optimized NLP model at the core of this system is able to identify and extract PICO-related fields from publications on immunization with an average accuracy of 88% across five classes of text. Functionality is provided through a straightforward web interface.

BACKGROUND: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.
OBJECTIVE: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.
METHODS: Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model\'s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.
RESULTS: Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.
CONCLUSIONS: The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

BACKGROUND: Natural language processing (NLP) has become an emerging technology in health care that leverages a large amount of free-text data in electronic health records to improve patient care, support clinical decisions, and facilitate clinical and translational science research. Recently, deep learning has achieved state-of-the-art performance in many clinical NLP tasks. However, training deep learning models often requires large, annotated data sets, which are normally not publicly available and can be time-consuming to build in clinical domains. Working with smaller annotated data sets is typical in clinical NLP; therefore, ensuring that deep learning models perform well is crucial for real-world clinical NLP applications. A widely adopted approach is fine-tuning existing pretrained language models, but these attempts fall short when the training data set contains only a few annotated samples. Few-shot learning (FSL) has recently been investigated to tackle this problem. Siamese neural network (SNN) has been widely used as an FSL approach in computer vision but has not been studied well in NLP. Furthermore, the literature on its applications in clinical domains is scarce.
OBJECTIVE: The aim of our study is to propose and evaluate SNN-based approaches for few-shot clinical NLP tasks.
METHODS: We propose 2 SNN-based FSL approaches, including pretrained SNN and SNN with second-order embeddings. We evaluate the proposed approaches on the clinical sentence classification task. We experiment with 3 few-shot settings, including 4-shot, 8-shot, and 16-shot learning. The clinical NLP task is benchmarked using the following 4 pretrained language models: bidirectional encoder representations from transformers (BERT), BERT for biomedical text mining (BioBERT), BioBERT trained on clinical notes (BioClinicalBERT), and generative pretrained transformer 2 (GPT-2). We also present a performance comparison between SNN-based approaches and the prompt-based GPT-2 approach.
RESULTS: In 4-shot sentence classification tasks, GPT-2 had the highest precision (0.63), but its recall (0.38) and F score (0.42) were lower than those of BioBERT-based pretrained SNN (0.45 and 0.46, respectively). In both 8-shot and 16-shot settings, SNN-based approaches outperformed GPT-2 in all 3 metrics of precision, recall, and F score.
CONCLUSIONS: The experimental results verified the effectiveness of the proposed SNN approaches for few-shot clinical NLP tasks.