arXiv:2606.16074v1 Announce Type: new Abstract: Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research. Prior work introduced the PV-Miner benchmark and PVMinerLLM models for structured extraction. However, supervised fine-tuning (SFT) alone struggles with rare, fine-grained, and unevenly distributed errors, particularly in token-critical structured outputs. Results: We present PVminerLLM2, an improved set of LLMs for structured
The continuous improvement of large language models (LLMs) and advanced optimization techniques is enabling more precise and reliable extraction from complex, unstructured data, such as patient-generated text.
This development enhances the ability to derive actionable insights from patient experiences, crucial for patient-centered outcomes research and improving healthcare quality.
The accuracy and reliability of structured information extraction from patient voice data are significantly improved, reducing errors and broadening potential applications in healthcare analytics.
- · Healthcare researchers
- · Pharmaceutical companies
- · AI developers focused on healthcare
- · Patients (indirectly through better care)
- · Manual data extraction processes
- · Legacy natural language processing (NLP) systems in healthcare
PVminerLLM2 offers more robust and accurate structured data from patient narratives.
Improved data quality fuels more precise patient outcome studies and accelerates medical innovation.
Enhanced understanding of patient experiences could lead to more personalized treatment plans and a shift towards patient-centric healthcare models.
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