Discussions of Cannabis Over Patient Portal Secure Messaging: Content Analysis by Vishal A Shetty, Christina M Gregor, Lorraine D Tusing, Apoorva M Pradhan, Katrina M Romagnoli, Brian J Piper, Eric A Wright

“…We used an unsupervised natural language processing approach to construct a lexicon that identified messages explicitly discussing cannabis. …”
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Mixtec–Spanish Parallel Text Dataset for Language Technology Development by Hermilo Santiago-Benito, Diana-Margarita Córdova-Esparza, Juan Terven, Noé-Alejandro Castro-Sánchez, Teresa García-Ramirez, Julio-Alejandro Romero-González, José M. Álvarez-Alvarado

“…This article introduces a freely available Spanish–Mixtec parallel corpus designed to foster natural language processing (NLP) development for an indigenous language that remains digitally low-resourced. …”
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Positional embeddings and zero-shot learning using BERT for molecular-property prediction by Medard Edmund Mswahili, JunHa Hwang, Jagath C. Rajapakse, Kyuri Jo, Young-Seob Jeong

“…The success of transformer-based models, such as the bidirectional encoder representations from transformer (BERT) models, in natural language processing tasks has sparked growing interest from the domain of cheminformatics. …”
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Artificial Intelligence−Driven Analysis Identifies Anterior Cruciate Ligament Reconstruction, Hip Arthroscopy and Femoroacetabular Impingement Syndrome, and Shoulder Instability as... by Henry B.G. Baird, B.S., William Allen, M.D., Mauricio Gallegos, M.D., Cody C. Ashy, M.D., Harris S. Slone, M.D., W. Michael Pullen, M.D.

“…BERTopic, an advanced natural language processing tool implemented in Python via Jupyter Notebook, uses contextual embeddings and clustering algorithms to efficiently group large datasets into topics based on semantic similarity. …”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…The rapid expansion of dialectally unique Arabic material on social media and the internet highlights how important it is to categorize dialects accurately to maximize a variety of Natural Language Processing (NLP) applications. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications. …”
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Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults by Ard Hendriks, Coen Hacking, Hilde Verbeek, Sil Aarts

“…Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. …”
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Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data by Ahmed Ghassan Tawfiq AbuRaed, Emil Azuma Prikryl, Giuseppe Carenini, Naveed Zafar Janjua

“…ConclusionsOur study combines natural language processing techniques, including contextualized topic modeling and sentiment analysis, along with domain expert input, to provide detailed insights into public health monitoring and intervention. …”
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Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? by P. Sun, P. B. Holden, H. J. B. Birks, H. J. B. Birks

“…We considered three versions of the model: (1) a standard version of MEMLM, which uses only taxon abundance data; (2) MEMLMe, which uses only dimensionally reduced assemblage information, using a natural language-processing model (GloVe), to detect associations between taxa across the training data set; and (3) MEMLMc which incorporates both raw taxon abundance and dimensionally reduced summary (GloVe) data. …”
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Identification of Online Health Information Using Large Pretrained Language Models: Mixed Methods Study by Dongmei Tan, Yi Huang, Ming Liu, Ziyu Li, Xiaoqian Wu, Cheng Huang

“…With advances in artificial intelligence and natural language processing, pretrained large language models (LLMs) have shown promise in identifying and distinguishing misleading health information, although their effectiveness in this area remains underexplored. …”
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Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study by Mohammed Al-Garadi, Michele LeNoue-Newton, Michael E Matheny, Melissa McPheeters, Jill M Whitaker, Jessica A Deere, Michael F McLemore, Dax Westerman, Mirza S Khan, José J Hernández-Muñoz, Xi Wang, Aida Kuzucan, Rishi J Desai, Ruth Reeves

“…ObjectiveThe aim of the study is to develop scalable approaches using natural language processing (NLP) and large language models (LLMs) for the extraction of mortality information from publicly available web-based data sources, including social media platforms, crowdfunding websites, and web-based obituaries, and to evaluate their performance across various sources. …”
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Artificial intelligence in resuscitation: a scoping review by Drieda Zace, Federico Semeraro, Sebastian Schnaubelt, Jonathan Montomoli, Giuseppe Ristagno, Nino Fijačko, Lorenzo Gamberini, Elena G. Bignami, Robert Greif, Koenraad G. Monsieurs, Andrea Scapigliati

“…Machine learning was the most commonly used method (50% of studies), followed by deep learning and, less frequently, natural language processing. Reported performance was generally high, with AUROC values often exceeding 0.85; however, external validation was rare and real-world implementation limited. …”
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Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review by Christos Kokkotis, Ioannis Kansizoglou, Theodoros Stampoulis, Erasmia Giannakou, Panagiotis Siaperas, Stavros Kallidis, Maria Koutra, Christina Koutra, Anastasia Beneka, Evangelos Bebetsos

“…Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. …”
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Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu by Aisyah Awalina, Fitra Abdurrachman Bachtiar, Fitri Utaminingrum

“…Transformer models are currently widely applied to natural language processing because they have outstanding performance. …”
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Research progress and implications of the application of large language model in shared decision-making in China’s healthcare field by Xuejing Li, Xuejing Li, Xuejing Li, Sihan Chen, Sihan Chen, Sihan Chen, Meiqi Meng, Meiqi Meng, Meiqi Meng, Ziyan Wang, Ziyan Wang, Ziyan Wang, Hongzhan Jiang, Hongzhan Jiang, Hongzhan Jiang, Yufang Hao, Yufang Hao, Yufang Hao

“…Large Language Models (LLMs), with their powerful natural language processing capabilities, demonstrate unique advantages in enhancing communication efficiency, supporting personalized decision-making, and promoting multi-party collaboration. …”
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Joint optimization of AI large and small models for surface temperature and emissivity retrieval using knowledge distillation by Wang Dai, Kebiao Mao, Zhonghua Guo, Zhihao Qin, Jiancheng Shi, Sayed M. Bateni, Liurui Xiao

“…The rapid advancement of artificial intelligence in domains such as natural language processing has catalyzed AI research across various fields. …”
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Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study by Cristina Crocamo, Riccardo Matteo Cioni, Aurelia Canestro, Christian Nasti, Dario Palpella, Susanna Piacenti, Alessandra Bartoccetti, Martina Re, Valentina Simonetti, Chiara Barattieri di San Pietro, Maria Bulgheroni, Francesco Bartoli, Giuseppe Carrà

“…Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (NLP) and acoustic signal processing may support ongoing BD assessment within a mobile health (mHealth) framework. …”
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Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study. by Elizabeth Ajalo, David Mukunya, Ritah Nantale, Frank Kayemba, Kennedy Pangholi, Jonathan Babuya, Suzan Langoya Akuu, Amelia Margaret Namiiro, Yakobo Baddokwaya Nsubuga, Joseph Luwaga Mpagi, Milton W Musaba, Faith Oguttu, Job Kuteesa, Aloysius Gonzaga Mubuuke, Ian Guyton Munabi, Sarah Kiguli

“…<h4>Background</h4>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”
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Incorporating individually defined brain health priorities in clinical trial outcomes: the electronic Person‐Specific Outcome Measure approach in the United States by Stina Saunders, Ali Jannati, Shane Sheehan, Claudio Toro‐Serey, Sean Tobyne, Killian McManus, David Bates, John Showalter, Álvaro Pascual‐Leone

“…We used natural language processing (k‐means clustering of GloVe vectors) and chi‐squared tests to examine differences between the US and UK populations' answers. …”
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Early neural dysfunction reflected in degraded auditory cortex responses in pre-regression heterozygous Mecp2 rats by Yuko Tamaoki, Samantha L. Kroon, Brendan M. Williams, Jonathan R. Riley, Crystal T. Engineer

“…Affected individuals exhibit seizures, cognitive impairments, motor deficits, and difficulties in speech-language processing. Post-regression rodent models of Rett syndrome have been observed to follow similar regression, presenting sensory processing difficulties during auditory discrimination tasks, as well as degraded auditory cortical responses. …”
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Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study by Han Yang, Mingchen Li, Huixue Zhou, Yongkang Xiao, Qian Fang, Shuang Zhou, Rui Zhang

“… Abstract BackgroundLarge language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including medical question-answering (QA). …”
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