---
language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - medical
  - pharmacovigilance
  - vaccines
datasets:
  - chrisvoncsefalvay/vaers-outcomes
metrics:
  - accuracy
  - f1
  - precision
  - recall
dataset: chrisvoncsefalvay/vaers-outcomes
pipeline_tag: text-classification
widget:
  - text: >-
      Patient is a 90 y.o. male with a PMH of IPF, HFpEF, AFib (Eliquis),
      Metastatic Prostate Cancer who presented to Hospital 10/28/2023 following
      an unwitnessed fall at his assisted living. He was found to have an AKI,
      pericardial effusion, hypoxia, AMS, and COVID-19. His hospital course was
      complicated by delirium and aspiration, leading to acute hypoxic
      respiratory failure requiring BiPAP and transfer to the ICU. Palliative
      Care had been following, and after goals of care conversations on
      11/10/2023 the patient was transitioned to DNR-CC. Patient expired at 0107
      11/12/23.
    example_title: VAERS 2727645 (hospitalisation, death)
  - text: >-
      hospitalized for paralytic ileus a week after the vaccination; This
      serious case was reported by a physician via call center representative
      and described the occurrence of ileus paralytic in a patient who received
      Rota (Rotarix liquid formulation) for prophylaxis. On an unknown date, the
      patient received the 1st dose of Rotarix liquid formulation. On an unknown
      date, less than 2 weeks after receiving Rotarix liquid formulation, the
      patient experienced ileus paralytic (Verbatim: hospitalized for paralytic
      ileus a week after the vaccination) (serious criteria hospitalization and
      GSK medically significant). The outcome of the ileus paralytic was not
      reported. It was unknown if the reporter considered the ileus paralytic to
      be related to Rotarix liquid formulation. It was unknown if the company
      considered the ileus paralytic to be related to Rotarix liquid
      formulation. Additional Information: GSK Receipt Date: 27-DEC-2023 Age at
      vaccination and lot number were not reported. The patient of unknown age
      and gender was hospitalized for paralytic ileus a week after the
      vaccination. The reporting physician was in charge of the patient.
    example_title: VAERS 2728408 (hospitalisation)
  - text: >-
      Patient received Pfizer vaccine 7 days beyond BUD. According to Pfizer
      manufacturer research data, vaccine is stable and effective up to 2 days
      after BUD. Waiting for more stability data from PFIZER to determine if
      revaccination is necessary.
    example_title: VAERS 2728394 (no event)
  - text: >-
      Fever of 106F rectally beginning 1 hr after immunizations and lasting <24
      hrs. Seen at ER treated w/tylenol & cool baths.
    example_title: VAERS 25042 (ER attendance)
  - text: >-
      I had the MMR shot last week, and I felt a little dizzy afterwards, but it
      passed after a few minutes and I'm doing fine now.
    example_title: 'Non-sample example: simulated informal patient narrative (no event)'
  - text: >-
      My niece had the COVID vaccine. A few weeks later, she was T-boned by a
      drunk driver. She called me from the ER. She's fully recovered now,
      though.
    example_title: >-
      Non-sample example: simulated informal patient narrative (ER attendance,
      albeit unconnected)
---

# daedra

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) trained on the [VAERS adversome outcomes data set](https://huggingface.com/datasets/chrisvoncsefalvay/vaers-outcomes). 

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.3.2
- Tokenizers 0.15.1