I'm Clément Longeac, an engineering student working at the intersection of clinical NLP and biomedical AI — currently building hybrid extraction pipelines for French oncology records, on the way to a PhD in AI applied to biomedical research.
I'm a fourth-year student at ESIEE Paris, specializing in Data Science & AI, and enrolled in the school's Tremplin Recherche track — a program built for students heading toward doctoral research rather than a standard industry placement.
My work centers on making unstructured clinical text usable for research: extracting the biomarkers, staging information, and treatment details buried in French oncology reports, where general-purpose NLP tools consistently fail on domain vocabulary and document structure.
Alongside my studies I tutor mathematics, physics, and computer science through Complétude, and I'm preparing applications for a CIFRE industrial PhD in biomedical AI at Sorbonne University.
A hybrid named-entity recognition pipeline for French oncology clinical text.
Off-the-shelf NER models struggle badly on French clinical text: dense abbreviations, inconsistent formatting across hospital systems, and biomarker terminology that barely appears in general training data. DEMNE tackles this with a staged pipeline rather than a single model — each stage handles what it's actually good at, and only escalates to a heavier model when it has to.
The system is fine-tuned on DrBERT-7GB across three distinct French oncology corpora, and targets seven priority biomarker entity types used in treatment-relevant reporting.
Regex and lexicon matching catch high-confidence, low-ambiguity entities cheaply — dates, dosages, standard biomarker abbreviations.
A conditional random field models sequential structure the rules miss, using local context to resolve ambiguous spans.
DrBERT-7BG, fine-tuned across three oncology corpora, handles the entities that need real semantic and domain understanding.
Remaining conflicts and edge cases are routed to an LLM pass for final disambiguation before output.
Developing DEMNE, a hybrid clinical NER pipeline for French oncology text, as the core project of the Tremplin Recherche track ahead of PhD applications.
Developed autopkgtests using OpenCL for AMD GPU compatibility (pyopencl, nabu, ufo-filters, pyvkfft) across Debian/ROCm infrastructure on GitLab Salsa.
Teaching mathematics, physics, and computer science to secondary and university students alongside my engineering studies.
Longer write-ups on the methods, dead ends, and decisions behind the work above.
A walkthrough of where standard NER pipelines fail on French oncology reports, and what that implies for how DEMNE is structured.
The case for routing entities through rules, a CRF, and a fine-tuned transformer before ever calling an LLM — and what it costs to get wrong.