Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Irbaz Bin
[2511.20680] Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports
BMC Bioinformatics, Sep 2023 Longitudinal data on key cancer outcomes for clinical research, such as response to treatment and disease progression, are not captured in standard cancer registry reporting. Manual extraction of such outcomes from unstructured electronic health records is a slow, resource-intensive process. Natural language processing (NLP) methods can accelerate outcome annotation, but they require substantial labeled data.
Empirical evaluation of language modeling to ascertain cancer outcomes from clinical text reports - BMC Bioinformatics
The overall cohort for this analysis consisted of patients with cancer participating in a single-institution genomic profiling study [27], and relevant data consisted of imaging reports for each patient. Each report was treated as its own unit of analysis, and reports were divided, at the patient level, into training (80%), validation (10%), and test (10%) datasets. For language model pre-training on data from our institution, reports for all patients in the training set were included.
Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset - Nature Communications
Abstract To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Irbaz
Contact Irbaz, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack