README.md

---
base_model: microsoft/biogpt
library_name: peft
tags:
- base_model:adapter:microsoft/biogpt
- transformers
---

# biogpt_MIMIC_IV_in_hospital_mortality_prediction_IA3_ti

This model is designed to predict in-hospital mortality (i.e., likely face death in their upcoming / current visit) from **prior** hospital records. It is trained on clinical notes from **prior hospitalizations** on MIMIC-IV.
Model was trained on a novel tabular-infused IA3, whereby the pre-operative tabular features (e.g., patient demographics and insurance information) were used to initialize the newly introduced IA3 parameters.  

## Model Details

- **Data**: MIMIC-IV (available [here](https://physionet.org/content/mimiciv/2.2/))
- **Outcome**: In-hospital mortality (`True`/`False`)
- **Base Model**: [`microsoft/biogpt`](https://huggingface.co/microsoft/biogpt)

## How to use model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cja5553/biogpt_MIMIC_IV_in_hospital_mortality_prediction_IA3_ti")
model = AutoModelForSequenceClassification.from_pretrained("cja5553/biogpt_MIMIC_IV_in_hospital_mortality_prediction_IA3_ti")
```

Then you can use this function below to get one test point

```python
import torch

def get_outcome(tokenizer, model, text, device="cuda:0", max_length=512):

    device = torch.device(device)
    model = model.to(device)
    model.eval()

    inputs = tokenizer(
        text,
        return_tensors="pt",
        max_length=max_length,
        truncation=True,
        padding="max_length"
    ).to(device)

    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=-1)[0]  # (2,)

    probs = probs.detach().cpu().numpy()
    result = {
        "False": float(probs[0]),
        "True": float(probs[1])
    }

    return result

```

## Questions?

Contact me at [alba@wustl.edu](mailto:alba@wustl.edu)