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Call for papers - Natural language processing in medical informatics

Guest Editor

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

Submission Status: Open   |   Submission Deadline: 16 November 2024


BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Natural language processing in medical informatics. 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.

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 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
    Content type: Research Published on:

  2. 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
    Content type: Research Published on:

  3. 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
    Content type: Research Published on:

  4. 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
    Content type: Research Published on:

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

  5. 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
    Content type: Research Published on:

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.