HealthcareNLP: where are we and what is next?

• URL: http://arxiv.org/abs/2512.08617v1
• Date: Tue, 09 Dec 2025 14:01:51 GMT
• Title: HealthcareNLP: where are we and what is next?
• Authors: Lifeng Han, Paul Rayson, Suzan Verberne, Andrew Moore, Goran Nenadic,
• Abstract summary: The goal of this tutorial is to provide an introductory overview of the most important sub-areas of a patient- and resource-oriented HealthcareNLP.<n>A hands-on session will be included in the tutorial for the audience to use HealthcareNLP applications.
• Score: 17.864292280393297
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This proposed tutorial focuses on Healthcare Domain Applications of NLP, what we have achieved around HealthcareNLP, and the challenges that lie ahead for the future. Existing reviews in this domain either overlook some important tasks, such as synthetic data generation for addressing privacy concerns, or explainable clinical NLP for improved integration and implementation, or fail to mention important methodologies, including retrieval augmented generation and the neural symbolic integration of LLMs and KGs. In light of this, the goal of this tutorial is to provide an introductory overview of the most important sub-areas of a patient- and resource-oriented HealthcareNLP, with three layers of hierarchy: data/resource layer: annotation guidelines, ethical approvals, governance, synthetic data; NLP-Eval layer: NLP tasks such as NER, RE, sentiment analysis, and linking/coding with categorised methods, leading to explainable HealthAI; patients layer: Patient Public Involvement and Engagement (PPIE), health literacy, translation, simplification, and summarisation (also NLP tasks), and shared decision-making support. A hands-on session will be included in the tutorial for the audience to use HealthcareNLP applications. The target audience includes NLP practitioners in the healthcare application domain, NLP researchers who are interested in domain applications, healthcare researchers, and students from NLP fields. The type of tutorial is "Introductory to CL/NLP topics (HealthcareNLP)" and the audience does not need prior knowledge to attend this. Tutorial materials: https://github.com/4dpicture/HealthNLP

Related papers

• What Can Natural Language Processing Do for Peer Review? [173.8912784451817]
  In modern science, peer review is widely used, yet it is hard, time-consuming, and prone to error. Since the artifacts involved in peer review are largely text-based, Natural Language Processing has great potential to improve reviewing. We detail each step of the process from manuscript submission to camera-ready revision, and discuss the associated challenges and opportunities for NLP assistance.
  arXiv  Detail & Related papers  (2024-05-10T16:06:43Z)
• Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends [29.91962119984834]
  We review recent advances in NLP with a focus on solving problems relevant to the education domain.<n>We present a taxonomy of NLP in the education domain and highlight typical NLP applications including question answering, question construction, automated assessment, and error correction.<n>We conclude with five promising directions for future research, including generalization over subjects and languages, deployed LLM-based systems for education, adaptive learning for teaching and learning, interpretability for education, and ethical consideration of NLP techniques.
  arXiv  Detail & Related papers  (2024-01-15T07:48:42Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare [13.176351544342735]
  This paper develops an integrated framework that addresses data privacy and regulatory compliance challenges. It includes the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and substantiating the efficacy of the proposed approach.
  arXiv  Detail & Related papers  (2023-06-28T17:00:32Z)
• Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good [115.1507728564964]
  We introduce NLP4SG Papers, a scientific dataset with three associated tasks. These tasks help identify NLP4SG papers and characterize the NLP4SG landscape. We use state-of-the-art NLP models to address each of these tasks and use them on the entire ACL Anthology.
  arXiv  Detail & Related papers  (2023-05-09T14:16:25Z)
• Survey of NLP in Pharmacology: Methodology, Tasks, Resources, Knowledge, and Tools [0.0]
  The main objective of this work is to survey the recent use of NLP in the field of pharmacology. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers.
  arXiv  Detail & Related papers  (2022-08-22T12:10:27Z)
• Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
  Deep learning has been the mainstream technique in natural language processing (NLP) area. Deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
  arXiv  Detail & Related papers  (2022-05-03T13:58:38Z)
• A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing [7.966218734325912]
  We searched six databases, including biomedical research and computer science literature database. A total of 35 papers with 47 clinical NLP tasks met inclusion criteria between 2007 and 2021.
  arXiv  Detail & Related papers  (2021-12-07T22:49:58Z)
• Graph Neural Networks for Natural Language Processing: A Survey [64.36633422999905]
  We present a comprehensive overview onGraph Neural Networks (GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, which organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models.
  arXiv  Detail & Related papers  (2021-06-10T23:59:26Z)
• FedNLP: A Research Platform for Federated Learning in Natural Language Processing [55.01246123092445]
  We present the FedNLP, a research platform for federated learning in NLP. FedNLP supports various popular task formulations in NLP such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling. Preliminary experiments with FedNLP reveal that there exists a large performance gap between learning on decentralized and centralized datasets.
  arXiv  Detail & Related papers  (2021-04-18T11:04:49Z)
• A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare [0.6193838300896449]
  The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain.
  arXiv  Detail & Related papers  (2020-07-17T17:50:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.