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Natural language processing in medical informatics

Guest Editor

Honghan Wu, PhD, Institute of Health Informatics, University College London, UK


BMC Medical Informatics and Decision Making called for submissions to our Collection on Natural language processing in medical informatics. The collection welcomed original research on recent advancements in NLP techniques, pre-trained language models, and clinical question-answering systems. As well as the development and evaluation of NLP algorithms for clinical documentation, information extraction, and decision support using electronic health records and patient monitoring data. We encourage authors to explore novel applications of NLP in precision medicine, mental health analysis, and multilingual healthcare settings. Additionally, we invited researchers to delve into explainable NLP approaches that enhance transparency and interpretability, paving the way for safer and more reliable clinical decision-making.

Meet the Guest Editor

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Honghan Wu, PhD, Institute of Health Informatics, University College London​​​​​​​, UK

Honghan Wu is an Associate Professor at the Institute of Health Informatics, University College London, United Kingdom and a Fellow of The Alan Turing Institute. His current research interest is in the area of AI in medicine, focusing on using deep learning, natural language processing and knowledge graph technologies for facilitating health and care. He plays technical leadership roles in several Health Data Research UK funded initiatives including National Text Analytics project. He works closely with National Health Service (NHS) organisations across the UK to use AI technologies in facilitating research and care.
 


About the Collection

BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Natural language processing in medical informatics. Recent developments in "Natural Language Processing in medical informatics” - NLP for medical informatics hold tremendous promise in shaping the future of healthcare. As NLP models become increasingly sophisticated and contextually aware, they can provide valuable insights into personalized treatment options based on patients' unique genetic profiles and medical histories. These advancements have the potential to revolutionize precision medicine, bringing us closer to delivering tailored therapies and improving patient outcomes. Furthermore, the integration of NLP with speech recognition technologies opens up exciting possibilities in real-time clinical applications, enabling seamless communication between healthcare providers and patients, and enhancing the overall quality of care. 

The collection welcomes original research on recent advancements in NLP techniques, pre-trained language models, and clinical question-answering systems. As well as the development and evaluation of NLP algorithms for clinical documentation, information extraction, and decision support using electronic health records and patient monitoring data. We encourage authors to explore novel applications of NLP in precision medicine, mental health analysis, and multilingual healthcare settings. Additionally, we invite researchers to delve into explainable NLP approaches that enhance transparency and interpretability, paving the way for safer and more reliable clinical decision-making.


Image credit: Iurii Motov / Getty Images / iStock.com 

  1. The automated processing of Electronic Health Records (EHRs) poses a significant challenge due to their unstructured nature, rich in valuable, yet disorganized information. Natural Language Processing (NLP), p...

    Authors: Domenico Paolo, Carlo Greco, Alessio Cortellini, Sara Ramella, Paolo Soda, Alessandro Bria and Rosa Sicilia
    Citation: BMC Medical Informatics and Decision Making 2025 25:169
  2. Analysis of Electronic Health Records (EHRs) is crucial in real-world evidence (RWE), especially in oncology, as it provides valuable insights into the complex nature of the disease. The implementation of adva...

    Authors: Livia Lilli, Mario Santoro, Valeria Masiello, Stefano Patarnello, Luca Tagliaferri, Fabio Marazzi and Nikola Dino Capocchiano
    Citation: BMC Medical Informatics and Decision Making 2025 25:160
  3. The integration of big data and artificial intelligence (AI) in healthcare, particularly through the analysis of electronic health records (EHR), presents significant opportunities for improving diagnostic acc...

    Authors: Izzet Turkalp Akbasli, Ahmet Ziya Birbilen and Ozlem Teksam
    Citation: BMC Medical Informatics and Decision Making 2025 25:154
  4. In this era of active online communication, patients increasingly share their healthcare experiences, concerns, and needs across digital platforms. Leveraging these vast repositories of real-world information,...

    Authors: Hyewon Jeon, Su-Yeon Yu, Olga Chertkova, Hyejung Yun, Yi Lin Ng, Yan Yoong Lim, Irina Efimenko and Djoubeir Mohamed Makhlouf
    Citation: BMC Medical Informatics and Decision Making 2025 25:137
  5. Clinical machine learning research and artificial intelligence driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists...

    Authors: Bauke Arends, Melle Vessies, Dirk van Osch, Arco Teske, Pim van der Harst, René van Es and Bram van Es
    Citation: BMC Medical Informatics and Decision Making 2025 25:115
  6. In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize terminology through the Adverse Event Terminology Collection of the Japan ...

    Authors: Ayako Yagahara, Masahito Uesugi and Hideto Yokoi
    Citation: BMC Medical Informatics and Decision Making 2025 25:66
  7. Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, fr...

    Authors: Xiongwen Yang, Yi Xiao, Di Liu, Yun Zhang, Huiyin Deng, Jian Huang, Huiyou Shi, Dan Liu, Maoli Liang, Xing Jin, Yongpan Sun, Jing Yao, XiaoJiang Zhou, Wankai Guo, Yang He, WeiJuan Tang…
    Citation: BMC Medical Informatics and Decision Making 2025 25:36
  8. Medical narratives are fundamental to the correct identification of a patient’s health condition. This is not only because it describes the patient’s situation. It also contains relevant information about the...

    Authors: Juan G. Diaz Ochoa, Faizan E. Mustafa, Felix Weil, Yi Wang, Kudret Kama and Markus Knott
    Citation: BMC Medical Informatics and Decision Making 2024 24:409
  9. [18F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can cu...

    Authors: Stephen H. Barlow, Sugama Chicklore, Yulan He, Sebastien Ourselin, Thomas Wagner, Anna Barnes and Gary J.R. Cook
    Citation: BMC Medical Informatics and Decision Making 2024 24:396
  10. The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording...

    Authors: Andrew Houston, Sophie Williams, William Ricketts, Charles Gutteridge, Chris Tackaberry and John Conibear
    Citation: BMC Medical Informatics and Decision Making 2024 24:371
  11. Embedding machine learning workflows into real-world hospital environments is essential to ensure model alignment with clinical workflows and real-world data. Many non-healthcare industries undergoing digital ...

    Authors: Joshua Au Yeung, Anthony Shek, Thomas Searle, Zeljko Kraljevic, Vlad Dinu, Mart Ratas, Mohammad Al-Agil, Aleksandra Foy, Barbara Rafferty, Vitaliy Oliynyk and James T. Teo
    Citation: BMC Medical Informatics and Decision Making 2024 24:356
  12. There are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal ap...

    Authors: Georgios Drougkas, Erwin M. Bakker and Marco Spruit
    Citation: BMC Medical Informatics and Decision Making 2024 24:354
  13. MRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar s...

    Authors: Rongpeng Dong, Xueliang Cheng, Mingyang Kang and Yang Qu
    Citation: BMC Medical Informatics and Decision Making 2024 24:343
  14. The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of patholog...

    Authors: Ken Cheligeer, Guosong Wu, Alison Laws, May Lynn Quan, Andrea Li, Anne-Marie Brisson, Jason Xie and Yuan Xu
    Citation: BMC Medical Informatics and Decision Making 2024 24:283
  15. Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate t...

    Authors: Amir Gandomi, Phil Wu, Daniel R Clement, Jinyan Xing, Rachel Aviv, Matthew Federbush, Zhiyong Yuan, Yajun Jing, Guangyao Wei and Negin Hajizadeh
    Citation: BMC Medical Informatics and Decision Making 2024 24:195
  16. Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The in...

    Authors: Oshin Miranda, Sophie Marie Kiehl, Xiguang Qi, M. Daniel Brannock, Thomas Kosten, Neal David Ryan, Levent Kirisci, Yanshan Wang and LiRong Wang
    Citation: BMC Medical Informatics and Decision Making 2024 24:154
  17. BERT models have seen widespread use on unstructured text within the clinical domain. However, little to no research has been conducted into classifying unstructured clinical notes on the basis of patient life...

    Authors: Hielke Muizelaar, Marcel Haas, Koert van Dortmont, Peter van der Putten and Marco Spruit
    Citation: BMC Medical Informatics and Decision Making 2024 24:151

    The Correction to this article has been published in BMC Medical Informatics and Decision Making 2024 24:169

  18. Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders fi...

    Authors: Tudor Groza, Harry Caufield, Dylan Gration, Gareth Baynam, Melissa A. Haendel, Peter N. Robinson, Christopher J. Mungall and Justin T. Reese
    Citation: BMC Medical Informatics and Decision Making 2024 24:30

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines  to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Natural language processing in medical informatics" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.