AI & LLM Engineering · maziyarpanahi/openmed

Fills in a model card for an OpenMed clinical NER or de-identification model from its evaluation reports: intended use, metrics, subgroups and limitations.

maziyarpanahi/openmed

Converts scanned faxes, images, CSV/TSV exports and C-CDA XML into clean text on-device, ready for OpenMed de-identification and named-entity recognition.

maziyarpanahi/openmed

Benchmark an OpenMed PII model with synthetic gold spans and report label-aware exact-span and grapheme recall without emitting identifier surfaces.

maziyarpanahi/openmed

Score an OpenMed clinical or biomedical NER model against a user-supplied gold corpus with entity-level precision, recall, and F1, then break errors down per label.

maziyarpanahi/openmed

Combine OpenMed clinical NLP with Microsoft Presidio, spaCy, or LangChain through OpenMed's built-in interop adapter registry (openmed.interop).

maziyarpanahi/openmed

Scaffold a synthetic gold-standard annotation project for evaluating OpenMed NER and de-identification models — label schema, annotation guidelines, BRAT or Label Studio config, and disjoint…

maziyarpanahi/openmed

Orient and bootstrap any project that uses OpenMed, the on-device clinical and biomedical NLP library, for named-entity recognition, PHI de-identification, FHIR export, and evaluation.

maziyarpanahi/openmed

Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts.

maziyarpanahi/openmed

Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1.

maziyarpanahi/openmed

Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyzetext.

maziyarpanahi/openmed

Load OpenMed clinical/biomedical NER models from the Hugging Face Hub or a local path and reuse them efficiently across calls.

maziyarpanahi/openmed

Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment.

maziyarpanahi/openmed

Run OpenMed models fully on-device with the MLX (Apple Silicon), CoreML (iOS/macOS), or ONNX/WebGPU (cross-platform/browser) backends, including convert-quantize-run workflows.

maziyarpanahi/openmed

Extract arbitrary, custom entity types from clinical or biomedical text with no fine-tuning using OpenMed's GLiNER / GLiNER2 zero-shot support.

maziyarpanahi/openmed