meds-evaluation 0.0.1

A module for evaluating the predictions of the models trained on MEDS datasets.

MEDS Evaluation

Evaluation API for the MEDS Decentralized Extensible Validation MEDS-DEV Benchmark.

[!NOTE] This is a work-in-progress package and currently only supports evaluation of binary classification tasks.

Intended usage

MEDS Evaluation pipeline is intended to be used together with MEDS-DEV, but can also be adapted to use as a standalone package.

Please refer to the MEDS-DEV tutorial to learn how to extract and prepare the data in the MEDS format and obtain model predictions ready to be evaluated.

Prediction schema

Inputs to MEDS Evaluation must follow the prediction schema, which by default has five fields:

1. subject_id: ID of the subject (patient) associated with the event
2. prediction_time: time at which the prediction as being made
3. boolean_value: ground truth boolean label for the prediction task
4. predicted_boolean_value (optional): predicted boolean label generated by the model
5. predicted_boolean_probability (optional): predicted probability logits generated by the model

This is equivalent to the following polars schema:

Schema(
    [
        ("subject_id", Int64),
        ("prediction_time", Datetime(time_unit="us")),
        ("boolean_value", Boolean),
        ("predicted_boolean_value", Boolean),
        ("predicted_boolean_probability", Float64),
    ]
)

Note that while predicted_boolean_value and predicted_boolean_probability are optional, at least one of them must be present and contain non-null values in order to generate the results. In addition, a schema can contain additional fields but at the moment these will not be used in MEDS Evaluation.