Yigit Gul is a Data Scientist and Scala/Python Developer at John Snow Labs, delivering hands-on projects in Healthcare and Life Science with a focus on clinical NLP and AI. He has a strong background in every aspect of data science including machine learning, artificial intelligence, and big data, with hands-on expertise in model training, fine-tuning, LLM evaluation and benchmarking, and large-scale Spark pipeline optimization across major cloud platforms. Holding a Bachelor's degree in Industrial Engineering and backed by extensive experience in Java backend development, Yigit brings a strong software engineering foundation to applied AI research. He has authored papers in peer-reviewed journals and conferences on clinical NLP topics including assertion detection, clinical text de-identification, LLM-based clinical applications, and OCR for healthcare use cases.
We benchmarked OpenAI Privacy Filter against a John Snow Labs de-identification pipeline on 381,959 tokens of real clinical text. The John Snow Labs pipeline reached 0.95 F1 on PHI detection...
The latest version of TextMatcher in Healthcare NLP introduces powerful linguistic enhancements such as lemmatization, stemming, stopwords removal, and token shuffling. These new features provide a flexible yet efficient way...
Assertion status detection is critical in clinical NLP but often overlooked, leading to underperformance in commercial solutions like AWS Medical Comprehend, Azure AI Text Analytics, and GPT-4o. We developed advanced...
Contextual Entity Ruler in Spark NLP refines entity recognition by applying context-aware rules to detected entities. It updates entities using customizable patterns, regex, and scope windows. It boosts accuracy by...
This blog post explores using Healthcare NLP, a powerful NLP library, for clinical text analysis. It focuses on Contextual Assertion for clinical text analysis, which significantly boosts accuracy in identifying...
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