Harnessing Natural Language Processing to Transform Medical Data Interpretation
Discover how natural language processing is revolutionizing cancer diagnosis and treatment planning by extracting critical information from unstructured medical notes, enhancing personalized care and research.
Imagine a computer system capable of analyzing a patient's medical notes to extract crucial details that can inform treatment decisions. At MUSC Hollings Cancer Center, researchers led by Dr. Jihad Obeid and Dr. Mario Fugal are utilizing advanced artificial intelligence (AI), specifically natural language processing (NLP), to unlock insights hidden within unstructured medical records. This innovative approach aims to improve personalized treatment for cancer patients, particularly those with brain metastases originating from cancers in other parts of the body like the lungs, breast, or skin.
Traditional methods rely heavily on ICD codes to identify cancer types, but these codes often lack the granular detail necessary for precise treatment planning. For instance, they can't distinguish between subtypes or location-specific characteristics of tumors, which are vital for tailoring radiation therapy. The clinical notes written by healthcare providers contain nuanced information—like tumor location, size, and specific pathology—that are often overlooked by standardized codes.
The research team developed an NLP model capable of reading and interpreting these clinician notes. Tested on over 82,000 records from more than 1,400 patients treated with stereotactic radiosurgery (SRS), the AI demonstrated remarkable accuracy—correctly identifying the primary cancer type in over 90% of cases, and nearly 97% for common cancers such as lung, breast, and skin cancers. This level of precision surpasses traditional coding methods and allows for more accurate classification, which is essential for effective treatment planning.
Why is this important? Because knowing the origin of brain metastases informs radiation dose decisions. Different cancers respond differently to radiation; lung cancers are highly sensitive, requiring lower doses, while kidney cancers tend to resist radiation and need different strategies. Precise identification minimizes the risks of overtreatment or undertreatment, protecting healthy tissue and reducing side effects.
Furthermore, this NLP approach is designed to be lightweight and scalable, making it accessible for medical institutions with limited resources. It does not require extensive datasets or heavy computational power, addressing many ethical and practical concerns associated with larger AI models.
The potential applications extend beyond classification; future research aims to employ NLP to predict complications like radiation necrosis, helping clinicians intervene earlier. Overall, this technology signifies a major advancement toward data-driven, personalized cancer care, leveraging routine medical records for rapid, accurate insights.
As health care continues to embrace digital transformation, NLP offers a promising avenue for extracting meaningful data from unstructured records—paving the way for faster research, improved treatment outcomes, and more efficient clinical workflows.
[source: https://medicalxpress.com/news/2025-07-natural-language-aid-doctors.html]
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