# 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_id_token>> <<lab_id_token>><<lab_value_bin>>`

Example: For a lab value of type 'Bic' with a normalized value in bin 'C', the token might be Bic BicC.
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

David Restrepo

MIT Critical Data - MIT