%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
En Core Med7 Trf
E
En Core Med7 Trf
Developed by kormilitzin
en_core_med7_trf is a clinical natural language processing model based on spaCy, specifically designed to identify and classify medication-related named entities from electronic health records.
Downloads 497
Release Time : 3/2/2022
Model Overview
This model focuses on extracting medication information from clinical texts, capable of identifying key details such as dosage, drug names, duration, dosage form, frequency, route of administration, and strength.
Model Features
High-precision Medication Information Extraction
Achieves an F1 score of 90.33 in medication-related named entity recognition tasks.
Optimized for Clinical Texts
The model is specifically optimized for clinical texts in electronic health records.
Recognition of Seven Medication-related Entities
Capable of identifying seven key medication-related entities: dosage, drug, duration, dosage form, frequency, route of administration, and strength.
Model Capabilities
Clinical Text Analysis
Medication Information Extraction
Named Entity Recognition
Use Cases
Healthcare
Electronic Health Record Analysis
Automatically extract medication information from patient electronic health records
Improves the efficiency of automated processing of medical records
Clinical Research Support
Assist clinical researchers in quickly analyzing medication data from large volumes of medical records
Accelerates the data collection process for clinical research
Transformers
PyTorchFeatured Recommended AI Models
Š 2025AIbase