Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods. by Zasim Azhar Siddiqui, Maryam Pathan, Sabina Nduaguba, Traci LeMasters, Virginia G Scott, Usha Sambamoorthi, Jay S Patel

“…This dataset was subsequently employed to train, test, and validate a Named Entity Recognition (NER) Natural Language Processing (NLP) application.<h4>Results</h4>After data preparation, 80,811 posts were collected: 500 for annotation guideline development, 2,000 for NLP application development, and the remaining 78,311 for deploying the application. …”
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WASPAS-Based Natural Language Processing Method for Handling Content Words Extraction and Ranking Issues: An Example of SDGs Corpus by Liang-Ching Chen, Kuei-Hu Chang, Jeng-Fung Hung

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Natural Language Processing Chatbot–Based Interventions for Improvement of Diet, Physical Activity, and Tobacco Smoking Behaviors: Systematic Review by Jing Chen, Run-Ze Hu, Yu-Xuan Zhuang, Jia-Qi Zhang, Rui Shan, Yang Yang, Zheng Liu

“…ObjectiveWe aimed to explore the effects of natural language processing (NLP) chatbot–based interventions on improving diet, physical activity, and tobacco smoking behaviors in the general population and to evaluate the chatbot use behaviors during the implementation process. …”
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Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions by Andrea Lastrucci, Enrico Giarnieri, Elisabetta Carico, Daniele Giansanti

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Incremental and transformational climate change adaptation factors in agriculture worldwide: A comparative analysis using natural language processing. by Sofia Gil-Clavel, Thorid Wagenblast, Tatiana Filatova

“…We address this gap by performing a comparative analysis of factors associated with farmers' climate change adaptation in both quantitative and qualitative literature using Natural Language Processing and generalized linear models. By retrieving publications from Scopus, we derive a database with metadata and associations from both quantitative and qualitative findings, focusing on climate change adaptation of farmers. …”
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Editorial: Natural language processing and artificial intelligence tools to explore the relationship between language and schizophrenia from diagnosis to care by Olivier Dufor, Amir Hossein Nikzad, Amir Hossein Nikzad, Valeria Lucarini, Valeria Lucarini, Valeria Lucarini, Christophe Lemey, Christophe Lemey, Christophe Lemey

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Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning by Junlong Ma, Heng Chen, Ji Sun, Juanjuan Huang, Gefei He, Guoping Yang

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Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish. by Andrea Garcia-Lopez, Juliana Cuervo-Rojas, Juan Garcia-Lopez, Fernando Giron-Luque

“…We developed and validated Natural Language Processing (NLP) and machine learning models to classify the status of kidney allografts in unstructured text in EMRs written in Spanish.…”
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Supervised Natural Language Processing Classification of Violent Death Narratives: Development and Assessment of a Compact Large Language Model by Susan T Parker

“… Abstract BackgroundThe recent availability of law enforcement and coroner or medical examiner reports for nearly every violent death in the United States expands the potential for natural language processing (NLP) research into violence. …”
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A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions by Babita Pandey, Devendra Kumar Pandey, Brijendra Pratap Mishra, Wasiur Rhmann

Subjects: “…Medical natural language processing…”
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A patient-centered approach to developing and validating a natural language processing model for extracting patient-reported symptoms by Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Sakura Yokoyama, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Hayato Kizaki, Masami Tsuchiya, Shungo Imai, Satoko Hori

Subjects: “…Natural language processing…”
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Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation by Tatsuki Hasegawa, Hayato Kizaki, Keisho Ikegami, Shungo Imai, Yuki Yanagisawa, Shuntaro Yada, Eiji Aramaki, Satoko Hori

“…Many screening models using natural language processing technology have been implemented to scrutinize articles based on titles and abstracts. …”
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Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study. by Phillip Park, Yeonho Choi, Nayoung Han, Ye-Lin Park, Juyeon Hwang, Heejung Chae, Chong Woo Yoo, Kui Son Choi, Hyun-Jin Kim

“…<h4>Objective</h4>For the data extraction of breast cancer pathology reports in a single institute, we assessed the accuracy of methods between regular expression and natural language processing (NLP).<h4>Methods</h4>A total of 1,215 breast cancer pathology reports were annotated for NLP model development. …”
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Natural Language Processing-Based Deep Learning to Predict the Loss of Consciousness Event Using Emergency Department Text Records by Hang A. Park, Inyeop Jeon, Seung-Ho Shin, Soo Young Seo, Jae Jun Lee, Chulho Kim, Ju Ok Park

Subjects: “…natural language processing…”
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Advances in Amazigh Language Technologies: A Comprehensive Survey Across Processing Domains by Oussama Akallouch, Mohammed Akallouch, Khalid Fardousse

Subjects: “…Amazigh language processing…”
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Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study by Hunki Paek, Richard H Fortinsky, Kyeryoung Lee, Liang-Chin Huang, Yazeed S Maghaydah, George A Kuchel, Xiaoyan Wang

“…We used a customized deep learning–based natural language processing (NLP) pipeline to extract clinical information from EHR data, focusing on cognition-related symptoms, primary caregiver relation, and medication usage. …”
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Understanding veterinary practitioners' responses to adverse events using a combined grounded theory and netnographic natural language processing approach. by Julie Gibson, Catherine Oxtoby, Marnie L Brennan, Kate White

“…Experiencing stress, externalising facts and feelings, morally contextualising events and catalysing personal and professional improvements were identified as components of practitioners' response. Natural language processing content analysis of posts regarding involvement in adverse events (n = 572) written by members of a veterinary member-only Facebook group was also performed, to categorise and count words within texts based on underlying meaning. …”
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Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health by Avijit Mitra, Kun Chen, Weisong Liu, Ronald C. Kessler, Hong Yu

“…This study investigates the impact of SBDH identified from both structured and unstructured data utilizing a natural language processing (NLP) system on suicide prediction at 7, 30, 90, and 180 days post-discharge. …”
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Mammography reporting dataset with BI-RADS system for natural language processing applications: Addressing public data gaps in SpanishZENODO by José Luis Vázquez Noguera, Alejandro Torres-Hurtado, Helena Gómez-Adorno, Julio César Mello-Román, Enrique Javier Fleitas-Alvarez, Federico Fernando Espinola Schulze, Miguel Garcia-Torres, Carlos Domingo Méndez Gaona, Pedro Esteban Gardel Sotomayor, Silvia Vázquez Noguera, Norma Elizabeth Zaracho Amarilla, Oxades Wilfrido Gamarra Esquivel

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Approaches to Extracting Patterns of Service Utilization for Patients with Complex Conditions: Graph Community Detection vs. Natural Language Processing Clustering by Jonas Bambi, Hanieh Sadri, Ken Moselle, Ernie Chang, Yudi Santoso, Joseph Howie, Abraham Rudnick, Lloyd T. Elliott, Alex Kuo

“…Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern detection analyses were conducted, employing (1) graph community detection algorithms, (2) natural language processing (NLP) clustering, and (3) a hybrid NLP–graph method. …”
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