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Using Natural Language Processing to Identify Symptomatic Adverse Events in Pediatric Oncology: Tutorial for Clinician Researchers
Journal article   Open access   Peer reviewed

Using Natural Language Processing to Identify Symptomatic Adverse Events in Pediatric Oncology: Tutorial for Clinician Researchers

Clifton P Thornton, Maryam Daniali, Lei Wang, Spandana Makeneni and Allison Barz Leahy
JMIR bioinformatics and biotechnology, v 6, e70751
24 Jul 2025
DOI: https://doi.org/10.2196/70751
PMID: 41342127
url
https://doi.org/10.2196/70751View
Published, Version of Record (VoR) Open

Abstract

AI applications in genomics and pathology informatics, including ‘Generative AI’ Artificial Intelligence Children and Young People With Cancer, Pediatric Oncology Natural Language Processing Theme Issue 2025: Artificial Intelligence in Oncology Tutorial
Artificial intelligence (AI) is poised to become an integral component in health care research and delivery, promising to address complex challenges with unprecedented efficiency and precision. However, many clinicians lack training and experience with AI, and for those who wish to incorporate AI into research and practice, the path forward remains unclear. Technical barriers, institutional constraints, and lack of familiarity with computer and data science frequently stall progress. In this tutorial, we present a transparent account of our experiences as a newly established interdisciplinary team of clinical oncology researchers and data scientists working to develop a natural language processing model to identify symptomatic adverse events during pediatric cancer therapy. We outline the key steps for clinicians to consider as they explore the utility of AI in their inquiry and practice, including building a digital laboratory, curating a large clinical dataset, and developing early-stage AI models. We emphasize the invaluable role of institutional support, including financial and logistical resources, and dedicated and innovative computer and data scientists as equal partners in the research team. Our account highlights both facilitators and barriers encountered spanning financial support, learning curves inherent with interdisciplinary collaboration, and constraints of time and personnel. Through this narrative tutorial, we intend to demystify the process of AI research and equip clinicians with actionable steps to initiate new ventures in oncology research. As AI continues to reshape the research and practice landscapes, sharing insights from past successes and challenges will be essential to informing a clear path forward.

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