transformers safetensors bert fill-mask autotrain_compatible endpoints_compatible region:us
# Model Details

#### Model Name: NumericBERT

#### Model Type: Transformer

#### Architecture: BERT

#### Training Method: Masked Language Modeling (MLM)

#### Training Data: MIMIC IV Lab values data

#### Training Hyperparameters:

- **Optimizer:** AdamW
- **Learning Rate:** 5e-5
- **Masking Rate:** 20%
- **Tokenization:** Custom numeric-to-text mapping using the TextEncoder class

### Text Encoding Process

**Overview:** Non-negative integers are converted into uppercase letter-based representations, allowing numerical values to be expressed as sequences of letters.

**Normalization and Binning:**
- **Method:** Log normalization and splitting into 10 bins.
- **Representation:** Each bin is represented by a letter (A-J).

### Token Construction:

- **Format:** `<<lab_value_bin>>`
- **Example:** For a lab value with a normalized value in bin 'C', the token might be `C`.
- **Columns Used:** 'Bic', 'Crt', 'Pot', 'Sod', 'Ure', 'Hgb', 'Plt', 'Wbc'.

### Training Data Preprocessing

- **Column Selection:** Numerical values from selected lab values.
- **Text Encoding:** Numeric values are encoded into text using the process described above.
- **Masking:** 20% of the data is randomly masked during training.

### Model Output

- **Description:** Outputs predictions for masked values during training.
- **Format:** Contains the encoded text representing the predicted lab values.

### Limitations and Considerations

- **Numeric Data Representation:** The custom text representation may have limitations in capturing the intricacies of the original numeric data.
- **Training Data Source:** Performance may be influenced by the characteristics and biases inherent in the MIMIC IV dataset.
- **Generalizability:** The model's effectiveness outside the context of the training dataset is not guaranteed.

### Contact Information

- **Email:** davidres@mit.edu
- **Name:** David Restrepo
- **Affiliation:** MIT Critical Data - MIT
