100 skills found · Page 1 of 4
b2ihealthcare / Snow Owl:owl: Snow Owl Terminology Server - a production-ready, scalable, FHIR Terminology Service compliant server that supports SNOMED CT International and Extensions, LOINC, RxNorm, UMLS, ICD-10/11, custom code systems and many others
jackwasey / Icd*New maintainer/owner needed* Fast ICD-10 and ICD-9 comorbidities, decoding and validation in R. NB use main instead of master for default branch.
wardle / HermesA library and microservice implementing the health and care terminology SNOMED CT with support for cross-maps, inference, fast full-text search, autocompletion, compositional grammar and the expression constraint language.
k4m1113 / ICD 10 CSVA comma-separated file of 2018 ICD-10 codes.
IBM / Nlc Icd10 ClassifierA simple web app that shows how Watson's Natural Language Classifier (NLC) can classify ICD-10 code. The app is written in Python using the Flask framework and leverages the Watson Developer Cloud Python SDK
Cicatriiz / Healthcare MCP PublicA Model Context Protocol (MCP) server providing AI assistants with access to healthcare data and medical information tools, including FDA drug info, PubMed, medRxiv, NCBI Bookshelf, clinical trials, ICD-10, DICOM metadata, and a medical calculator.
bryand1 / Icd10 CmICD-10 CM medical classification list by the World Health Organization
StefanoTrv / Simple Icd 10 CMA simple python library for ICD-10-CM codes
shihjay2 / Nosh2NOSH ChartingSystem is an electronic health record system designed exclusively for doctors and patients. This is a new mobile-friendly version that is now based off of the Laravel PHP framework and jQuery. NOSH has FHIR, Bluebutton, ICD-10, GoodRX API, RXNorm API, Phaxio, and UMA support.
suamin / ICD BERTICD-BERT: Multi-label Classification of ICD-10 Codes with BERT (CLEF 2019)
kaneplusplus / Icd 10 Cm EmbeddingLLM Embeddings for ICD 10 Data
chaseliu / ICD 10 CNICD-10 Chinese Verion
spiros / Chronological Map PhenotypesMachine-readable version of electronic health record phenotypes for Kuan V. and Denaxas S. et al.
StefanoTrv / Simple Icd 10A simple python library for ICD-10 codes
AtlasCUMC / ICD10 ICD9 Codes ConversionNo description available
spiros / PhemapFunctions to map between ICD-10 terms and PheCodes for UK Biobank hospital electronic health records
FreedomIntelligence / Awesome Specialized Medical LLMsA collection of research on specialized medical LLMs for specific diseases and distinct medical specialties, organized by ICD-10 chapters.
Trexgiantsalamanderdoublechickenturtle / Ukb ICD10 Event ExtractionR functions for extracting diagnoses and first diagnosis dates from “Data-Field 41270” and “Data-Field 41280” in the UK Biobank, based on ICD-10 codes.
WhiteCoatAcademy / Icd10Dead simple, incredibly fast ICD-10 diagnosis code searching.
hltfbk / E3C CorpusE3C is a freely available multilingual corpus (Italian, English, French, Spanish, and Basque) of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, and training of information extraction systems. It consists of two types of annotations: (i) clinical entities: pathologies, symptoms, procedures, body parts, etc., according to standard clinical taxonomies (i.e. SNOMED-CT, ICD-10); and (ii) temporal information and factuality: events, time expressions, and temporal relations according to the THYME standard. The corpus is organised into three layers, with different purposes. Layer 1: about 25K tokens per language with full manual annotation of clinical entities, temporal information and factuality, for benchmarkingand linguistic analysis. Layer 2: 50-100K tokens per language with semi-automatic annotations of clinical entities, to be used to train baseline systems. Layer 3: about 1M tokens per language of non-annotated medical documents to be exploited by semi-supervised approaches. Researchers can use the benchmark training and test splits of our corpus to develop and test their own models. We trained several deep learning based models and provide baselines using the benchmark. Both the corpus and the built models will be available through the ELG platform.
