Build Grateful Patient Features

Academic Medical Centers (AMCs) track hospital visits across different service lines. Not all hospital visits are equal for philanthropy (e.g., Oncology correlates more strongly with giving than Urgent Care).

Using GratefulPatientFeaturizer

This HIPAA-safe transformer translates clinical encounter histories into powerful signals.

import pandas as pd
from philanthropy.preprocessing import GratefulPatientFeaturizer

# Clinical history (no Patient Identifiers needed for output features)
encounters = pd.DataFrame({
    "donor_id": [1, 1, 2],
    "discharge_date": ["2022-01-01", "2023-06-15", "2022-09-30"],
    "service_line": ["cardiac", "cardiac", "oncology"],
    "attending_physician_id": ["P1", "P2", "P3"],
})

# Donors in our current dataset
X = pd.DataFrame({"donor_id": [1, 2, 3]})

gpf = GratefulPatientFeaturizer(encounter_df=encounters)
out = gpf.fit_transform(X)
print(out)

Clinical Gravity Scores

The GratefulPatientFeaturizer automatically weights occurrences. You can provide a service_line_weights dictionary to prioritize specific clinical areas instead of using the defaults derived from AMC datasets.

The Solicitation Window

Patients in a 6-to-24 month window post-discharge are often the warmest prospects. The DischargeToSolicitationWindowTransformer measures proximity to this optimal sweet spot perfectly:

from philanthropy.preprocessing import DischargeToSolicitationWindowTransformer

window = DischargeToSolicitationWindowTransformer()
# X_out = window.fit_transform(X)