7 citations · 20 across the 10 of their papers we have counts for
12 papers
DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs
Mahmood Bayeshi, Veysel Kocaman, Muhammed Ali Naqvi +2
Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with explanations. Frontier LLMs are s…
A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models
Andrei Marian Feier, Veysel Kocaman, Yigit Gul +6
Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or ethically complex conditions c…
Specialty-Specific Medical Language Model for Immune-Mediated Diseases
Veysel Kocaman, Gursev Pirge, Yigit Gul +3
Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Terminology for immune-mediated and…
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?
Veysel Kocaman, Muhammed Santas, Yigit Gul +2
We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and Jo…
Beyond Negation Detection: Comprehensive Assertion Detection Models for Clinical NLP
Veysel Kocaman, Yigit Gul, M. Aytug Kaya +4
Assertion status detection is a critical yet often overlooked component of clinical NLP, essential for accurately attributing extracted medical facts. Past studies have narrowly fo…
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets
Veysel Kocaman, Hasham Ul Haq, David Talby
Recent research advances achieve human-level accuracy for de-identifying free-text clinical notes on research datasets, but gaps remain in reproducing this in large real-world sett…
Understanding COVID-19 News Coverage using Medical NLP
Ali Emre Varol, Veysel Kocaman, Hasham Ul Haq +1
Being a global pandemic, the COVID-19 outbreak received global media attention. In this study, we analyze news publications from CNN and The Guardian - two of the world's most infl…
Mining Adverse Drug Reactions from Unstructured Mediums at Scale
Hasham Ul Haq, Veysel Kocaman, David Talby
Adverse drug reactions / events (ADR/ADE) have a major impact on patient health and health care costs. Detecting ADR's as early as possible and sharing them with regulators, pharma…
Deeper Clinical Document Understanding Using Relation Extraction
Hasham Ul Haq, Veysel Kocaman, David Talby
The surging amount of biomedical literature & digital clinical records presents a growing need for text mining techniques that can not only identify but also semantically relate en…
Spark NLP: Natural Language Understanding at Scale
Veysel Kocaman, David Talby
Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML. It provides simple, performant and accurate NLP annotations for machine learning pipelines…
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
Veysel Kocaman, David Talby
Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We prese…
Biomedical Named Entity Recognition at Scale
Veysel Kocaman, David Talby
Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the…