Nicholas L. Rider

Nishad Kulkarni, Alexandra K. Martinson, Nicholas L. Rider et al.

Rare diseases such as inborn errors of immunity (IEI) require early diagnosis to prevent end organ damage and improve quality of life. Hurdles in accessing and curating large scale electronic health record (EHR) data limit routine data driven analyses to remain on the forefront of IEI and other rare disease trends. Development of machine learning (ML) algorithms in IEI for pattern recognition as well as published methodology examining how to systematically process and integrate complex medical data is limited. Our proposed pipeline, including data curation and ML clustering algorithms, is designed to recognize novel rare disease patterns and extract IEI- associated features from a national data registry. Our methodology for EHR data formatting and processing presents the pipeline that transforms raw immunologic lab data into vectors. This is further combined with hyperparameter tuning for diseases pattern recognition via clustering. This study refines IEI feature awareness, develops data tool kits for rare disease populations analysis, and expands on transforming complex medical records in data structures interpretable by unsupervised ML.

Enshuo Hsu, Martin Ugbala, Krishna Kumar Kookal et al.

Generative information extraction using large language models, particularly through few-shot learning, has become a popular method. Recent studies indicate that providing a detailed, human-readable guideline-similar to the annotation guidelines traditionally used for training human annotators can significantly improve performance. However, constructing these guidelines is both labor- and knowledge-intensive. Additionally, the definitions are often tailored to meet specific needs, making them highly task-specific and often non-reusable. Handling these subtle differences requires considerable effort and attention to detail. In this study, we propose a self-improving method that harvests the knowledge summarization and text generation capacity of LLMs to synthesize annotation guidelines while requiring virtually no human input. Our zero-shot experiments on the clinical named entity recognition benchmarks, 2012 i2b2 EVENT, 2012 i2b2 TIMEX, 2014 i2b2, and 2018 n2c2 showed 25.86%, 4.36%, 0.20%, and 7.75% improvements in strict F1 scores from the no-guideline baseline. The LLM-synthesized guidelines showed equivalent or better performance compared to human-written guidelines by 1.15% to 4.14% in most tasks. In conclusion, this study proposes a novel LLM self-improving method that requires minimal knowledge and human input and is applicable to multiple biomedical domains.