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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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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A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs by Xiaohao Mao, Yu Huang, Ye Jin, Lun Wang, Xuanzhong Chen, Honghong Liu, Xinglin Yang, Haopeng Xu, Xiaodong Luan, Ying Xiao, Siqin Feng, Jiahao Zhu, Xuegong Zhang, Rui Jiang, Shuyang Zhang, Ting Chen

“…PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases. …”
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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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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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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency by Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand, Reyaz Kausar Siddiqui, Rachana Mehta, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Arif Hussain, Ranjit Sah

“…The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. …”
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Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review by Maxime Sasseville, Steven Ouellet, Caroline Rhéaume, Malek Sahlia, Vincent Couture, Philippe Després, Jean-Sébastien Paquette, David Darmon, Frédéric Bergeron, Marie-Pierre Gagnon

“…Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. …”
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Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods by Lisa M Gandy, Lana V Ivanitskaya, Leeza L Bacon, Rodina Bizri-Baryak

“…ObjectivePopular sentiment analysis tools based on natural language processing (NLP; VADER [Valence Aware Dictionary for Sentiment Reasoning], TEXT2DATA [T2D], and Linguistic Inquiry and Word Count [LIWC-22]), and a large language model (ChatGPT 4.0) were compared with manually coded sentiment scores, as applied to the analysis of YouTube comments on videos discussing the opioid epidemic. …”
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Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society. by Muhammad Tayyab Zamir, Fida Ullah, Rasikh Tariq, Waqas Haider Bangyal, Muhammad Arif, Alexander Gelbukh

“…The methodology of this work includes gathering a dataset comprising fabricated news articles sourced from a corpus and subjected to the natural language processing (NLP) cycle. After applying some filters, a total of five machine learning classifiers and three deep learning classifiers were employed to forecast the sentiment of news articles, distinguishing between those that are authentic and those that are fabricated. …”
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Advancing health equity: evaluating AI translations of kidney donor information for Spanish speakers by Oscar A. Garcia Valencia, Charat Thongprayoon, Caroline C. Jadlowiec, Shennen A. Mao, Napat Leeaphorn, Pooja Budhiraja, Nadeen Khoury, Justin H. Pham, Iasmina M. Craici, Maria L. Gonzalez Suarez, Wisit Cheungpasitporn

“…ChatGPT, an AI language model with sophisticated natural language processing capabilities, has been identified as a promising tool for translating critical health information into Spanish. …”
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Analisis Perbandingan Model Bert Dan Xlnet Untuk Klasifikasi Tweet Bully Pada Twitter by Teuku Radillah, Okta Veza, Sarjon Defit

“…This study aims to compare the performance of two recent natural language processing models, namely BERT (Bidirectional Encoder Representations from Transformers) and XLNet, in the classification of tweets containing bullying. …”
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MathVision: An Accessible Intelligent Agent for Visually Impaired People to Understand Mathematical Equations by Muhammad Awais Ahmad, Tauqir Ahmed, Muhammad Aslam, Amjad Rehman, Faten S. Alamri, Saeed Ali Bahaj, Tanzila Saba

“…This TTS using natural language processing analyzes and processes the text then it converts this processed text into speech using digital signal processing technology. …”
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The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance by Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan, Muhammad Shafiq, Wenjie Fang, Rahat Ullah Khan, Mujeeb Ur Rahman, Xiaohui Li, Qiao-Li Lv, Bin Xu

“…Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. …”
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Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study by Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal, JaMor Hairston, Jeanne Powell, Drew Walker, Snigdha Peddireddy, Sahithi Lakamana, Selen Bozkurt, Matthew Reyna, Reza Sameni, Yunyu Xiao, Sangmi Kim, Rasheeta Chandler, Natalie Hernandez, Danielle Mowery, Rachel Wightman, Jennifer Love, Anthony Spadaro, Jeanmarie Perrone, Abeed Sarker

“…However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. …”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…<p>Deep learning models have demonstrated remarkable success in fields such as language processing and computer vision, routinely employed for tasks like language translation, image classification, and anomaly detection. …”
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Association of delayed asthma diagnosis with asthma exacerbations in children by Chung-Il Wi, MD, Euijung Ryu, PhD, Katherine S. King, MS, Jung Hyun Kwon, MD, PhD, Joshua T. Bublitz, BS, Miguel Park, MD, Sergio E. Chiarella, MD, Jason D. Greenwood, MD, MS, Thanai Pongdee, MD, Lynnea Myers, PhD, Björn Nordlund, PhD, Sunghwan Sohn, PhD, Elham Sagheb, MS, Bhavani Singh Agnikula Kshatriya, MS, Dave Watson, PhD, Hongfang Liu, PhD, Beverley J. Sheares, MD, MS, Carla M. Davis, MD, Wade Schulz, MD, PhD, Young J. Juhn, MD, MPH

“…We defined onset date as the date when subjects first met predetermined asthma criteria ascertained by an electronic health records–based natural language processing algorithm. Delay in diagnosis (DD) was defined as first diagnosis >30 days from onset date (vs timely diagnosis [TD] within 30 days). …”
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NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation by Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahadiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshang Wu, Jascha Sohl-Dickstein, Jinho Choi, Eduard Hovy, Ondřej Dušek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honoré, Ishan Jindal, Przemysław Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxine Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Meunnighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicholas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos Samus, Ananya Sai, Robin Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib Shamsi, Xudong Shen, Yiwen Shi, Haoyue Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, Aditya Srivatsa, Tony Sun, Mukund Varma, A Tabassum, Fiona Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Zijie Wang, Gloria Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyu Wu, Witold Wydmanski, Tianbao Xie, Usama Yaseen, Michael Yee, Jing Zhang, Yue Zhang

“… Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural language (NL) augmentation framework which supports the creation of transformations (modifications to the data) and filters (data splits according to specific features). …”
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Navigating Ethical Dilemmas Of Generative AI In Medical Writing by Qurrat Ulain Hamdan, Waleed Umar, Mahnoor Hasan

“…Generative AI in Medical Writing Generative AI tools or “chatbots” combine the adaptive learning capabilities of deep learning algorithms and natural language processing, resulting in a virtual assistant or aide that is capable of answering queries, following commands, and improving its responses according to the vast data available on the Internet in addition to user responses.3 This has allowed the accomplishment of various complex tasks within seconds that would otherwise require hours of trial and error. …”
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Association of glycemic control with Long COVID in patients with type 2 diabetes: findings from the National COVID Cohort Collaborative (N3C) by Jane E B Reusch, Rachel Wong, Nasia Safdar, Harold Lehmann, Brijesh Patel, Til Stürmer, Hongfang Liu, Peter Robinson, Elaine Hill, Richard Moffitt, Justin Guinney, Joel Gagnier, Cavin Ward-Caviness, Noha Sharafeldin, Justin Starren, Amit Saha, Lesley Cottrell, Melissa A Haendel, Margaret A Hall, Vignesh Subbian, Kristin Kostka, Farrukh M Koraishy, Andrew E Williams, Robert Hurley, Steve Johnson, Usman Sheikh, Rishi Kamaleswaran, Christopher Dillon, Michele Morris, Randeep Jawa, Hemalkumar Mehta, Benjamin Bates, Tellen D Bennett, Nabeel Qureshi, Katie Rebecca Bradwell, Federico Mariona, Adam B Wilcox, Adam M Lee, Alexis Graves, Amin Manna, Amy Olex, Andrea Zhou, Andrew Southerland, Andrew T Girvin, Anita Walden, Anjali A Sharathkumar, Benjamin Amor, Brian Hendricks, Caleb Alexander, Carolyn Bramante, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A Eichmann, Diego Mazzotti, Eilis Boudreau Don Brown, Elizabeth Zampino, Emily Carlson Marti, Emily R Pfaff, Evan French, Fred Prior, George Sokos, Greg Martin, Heidi Spratt, Hythem Sidky, JW Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel H Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L Rutter, Julie A McMurry, Karen Crowley, Kellie M Walters, Ken Wilkins, Kenneth R Gersing, Kenrick Dwain Cato, Kimberly Murray, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lili Portilla, Mariam Deacy, Mark M Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B Palchuk, Meredith Adams, Meredith Temple-O'Connor, Michael G Kurilla, Nicole Garbarini, Ofer Sadan, Patricia A Francis, Penny Wung Burgoon, Rafael Fuentes, Rebecca Erwin-Cohen, Richard A Moffitt, Richard L Zhu, Robert T Miller, Saiju Pyarajan, Sam G Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, T Shawn, Soko Setoguchi O'Neil, Stephanie S Hong, Tiffany Callahan, Umit Topaloglu, Valery Gordon, Warren A Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang, Samuel Soff, Yun Jae Yoo, Jared Davis Huling, Daniel Brannock, Zachary Butzin-Dozier, Alfred Jerrod Anzalone, Philip RO. Payne, Rena Patel

“…Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30–180 days after COVID-19. …”
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The Parallel Architecture—application and explanatory power for neurolinguistic research by Esther Odilia Breuer, Ferdinand Christoph Binkofski, Ferdinand Christoph Binkofski, Antonello Pellicano

“…Various linguistic models have been developed to systematize language processes and provide a structured framework for understanding the complex network of language production and reception. …”
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