Basic usage of CDSS to generate a protocol recommendation.

This example demonstrates how to use the DataLoader, DataProcessor, and CDSS to load data and generate a 7-day protocol plan for testing/CI purposes.

/home/runner/work/ai-cdss/ai-cdss/src/ai_cdss/processing/feature_builder.py:93: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df = df.groupby(by=[PATIENT_ID, SESSION_DATE], group_keys=False).apply(
   PATIENT_ID  PROTOCOL_ID       PPF  \
0           1            1  0.562178
1           1            2  0.965500
2           1            3  0.812396
3           1            4  0.719061
4           1            5  0.409213

                                             CONTRIB  ADHERENCE_RECENT  \
0  [0.252232, 0.03653, 0.118418, 0.147197, 0.007801]          0.699687
1   [0.119867, 0.02396, 0.00862, 0.381091, 0.431962]          0.722053
2   [0.34855, 0.076633, 0.02168, 0.243166, 0.122367]          0.768798
3   [0.026373, 0.13851, 0.007091, 0.486407, 0.06068]          0.795200
4  [0.139366, 0.047926, 0.094186, 0.101513, 0.026...          0.757253

   DELTA_DM  WEEKS_SINCE_START  SESSION_INDEX  USAGE  USAGE_WEEK  \
0  0.003929                 79             14     10           0
1 -0.003380                 79             11     10           0
2  0.007271                 79             12     10           0
3 -0.007977                 79             11     10           0
4  0.004960                 79             11     10           0

                    DAYS     SCORE
0        [2, 3, 4, 5, 6]  1.265794
1     [0, 1, 3, 4, 5, 6]  1.684172
2     [0, 1, 2, 3, 5, 6]  1.588465
3  [0, 1, 2, 3, 4, 5, 6]  1.506284
4     [0, 2, 3, 4, 5, 6]  1.171426

import sys

sys.path.append("..")
import pandas as pd
from ai_cdss.cdss import CDSS
from ai_cdss.constants import EWMA_ALPHA
from ai_cdss.loaders.mock_loader import DataLoaderMock
from ai_cdss.models import DataUnit, DataUnitName, DataUnitSet, Granularity
from ai_cdss.processing.processor import DataProcessor
from IPython.display import display

print(__doc__)

PATIENT_LIST = [1, 2, 3]

# Parameters
rgs_mode = "app"
weights = [1, 1, 1]
alpha = EWMA_ALPHA

n = 12
days = 7
protocols_per_day = 5

# Services
loader = DataLoaderMock(num_patients=5, num_protocols=5, num_sessions=10)
processor = DataProcessor()

# Execution
session = loader.load_session_data(patient_list=PATIENT_LIST)
timeseries = loader.load_timeseries_data(patient_list=PATIENT_LIST)
ppf = loader.load_ppf_data(patient_list=PATIENT_LIST)
patient = loader.load_patient_data(patient_list=PATIENT_LIST)
protocol_similarity = loader.load_protocol_similarity()

# Construct DataUnitSet for processing
units = [session, ppf, patient]
data_unit_set = DataUnitSet(units)

scores = processor.process_data(data_unit_set, scoring_date=pd.Timestamp.today())

# CDSS
cdss = CDSS(scoring=scores, n=n, days=days, protocols_per_day=protocols_per_day)

# Results
patient_id = 1
recommendation = cdss.recommend(
    patient_id=patient_id, protocol_similarity=protocol_similarity
)
recommendation.to_csv(f"recommendation_{patient_id}_new.csv", index=False)

with pd.option_context("display.max_columns", None):
    display(recommendation)