
# PubMed PICO extraction (NER model)

This is a fine-tuned multi-class NER model for PICO labels within PubMed biomedical database abstracts, trained with [NERDA](https://ebanalyse.github.io/NERDA/).

The mode predicts:

| Class | **F1-Score** | Precision | Recall |

|B-Patient | **0.72** | 0.69 | 0.76

|I-Patient | **0.77** | 0.74 | 0.81

|B-Intervention | **0.62** | 0.61 | 0.63 |

|I-Intervention | **0.70** | 0.70 | 0.71 |

|B-Control | **0.59** | 0.56 | 0.63 |

|I-Control | **0.62** | 0.64 | 0.61 |

|B-Outcome | **0.49** | 0.50 | 0.49 |

|I-Outcome | **0.56** | 0.63 | 0.51 |

{"language": ["en"], "license": "cc-by-2.0", "tags": ["pico", "pubmed", "ner", "pubmedbert"]}
pico pubmed ner pubmedbert en license:cc-by-2.0 region:us
TAGS
#pico #pubmed #ner #pubmedbert #en #license-cc-by-2.0 #region-us 


# PubMed PICO extraction (NER model)

This is a fine-tuned multi-class NER model for PICO labels within PubMed biomedical database abstracts, trained with NERDA.

The mode predicts:

| Class | F1-Score | Precision | Recall |

|B-Patient | 0.72 | 0.69 | 0.76

|I-Patient | 0.77 | 0.74 | 0.81

|B-Intervention | 0.62 | 0.61 | 0.63 |

|I-Intervention | 0.70 | 0.70 | 0.71 |

|B-Control | 0.59 | 0.56 | 0.63 |

|I-Control | 0.62 | 0.64 | 0.61 |

|B-Outcome | 0.49 | 0.50 | 0.49 |

|I-Outcome | 0.56 | 0.63 | 0.51 |

# PubMed PICO extraction (NER model)

This is a fine-tuned multi-class NER model for PICO labels within PubMed biomedical database abstracts, trained with NERDA.

The mode predicts:

| Class | F1-Score | Precision | Recall |

|B-Patient | 0.72 | 0.69 | 0.76

|I-Patient | 0.77 | 0.74 | 0.81

|B-Intervention | 0.62 | 0.61 | 0.63 |

|I-Intervention | 0.70 | 0.70 | 0.71 |

|B-Control | 0.59 | 0.56 | 0.63 |

|I-Control | 0.62 | 0.64 | 0.61 |

|B-Outcome | 0.49 | 0.50 | 0.49 |

|I-Outcome | 0.56 | 0.63 | 0.51 |
TAGS
#pico #pubmed #ner #pubmedbert #en #license-cc-by-2.0 #region-us 
 # PubMed PICO extraction (NER model)

This is a fine-tuned multi-class NER model for PICO labels within PubMed biomedical database abstracts, trained with NERDA.

The mode predicts:

| Class | F1-Score | Precision | Recall |

|B-Patient | 0.72 | 0.69 | 0.76

|I-Patient | 0.77 | 0.74 | 0.81

|B-Intervention | 0.62 | 0.61 | 0.63 |

|I-Intervention | 0.70 | 0.70 | 0.71 |

|B-Control | 0.59 | 0.56 | 0.63 |

|I-Control | 0.62 | 0.64 | 0.61 |

|B-Outcome | 0.49 | 0.50 | 0.49 |

|I-Outcome | 0.56 | 0.63 | 0.51 |
TAGS
#pico #pubmed #ner #pubmedbert #en #license-cc-by-2.0 #region-us 
# PubMed PICO extraction (NER model)

This is a fine-tuned multi-class NER model for PICO labels within PubMed biomedical database abstracts, trained with NERDA.

The mode predicts:

| Class | F1-Score | Precision | Recall |

|B-Patient | 0.72 | 0.69 | 0.76

|I-Patient | 0.77 | 0.74 | 0.81

|B-Intervention | 0.62 | 0.61 | 0.63 |

|I-Intervention | 0.70 | 0.70 | 0.71 |

|B-Control | 0.59 | 0.56 | 0.63 |

|I-Control | 0.62 | 0.64 | 0.61 |

|B-Outcome | 0.49 | 0.50 | 0.49 |

|I-Outcome | 0.56 | 0.63 | 0.51 |