Connection Learning Healthcare System · EPINET · research tool

Predicted coordinated specialty care outcomes from admission data

Enter a participant's admission assessment to reproduce the predictions of the three random forest models described in Development and Preliminary Validation of Models to Predict Risk of Disengagement from Coordinated Specialty Care Programs for First Episode Psychosis. Leave any item blank if it was not recorded; the models were trained with missing values and use them directly.

Development data: 1,194 discharged participants, 23 programs (PA, MD), 2021–2025 Completion vs. disengagement: mean held-out AUC 0.67 Not externally validated
For research use only. These models have been internally validated only. Their discrimination is modest, and they have not been tested in new programs or later time periods. Do not use the output to make or withhold clinical decisions about an individual.

Score a file

Upload a CSV with one row per participant. Column names must match the variable names in the reference table below (an optional ID column is carried through). Blank cells, NA and columns that are absent are treated as missing. Values are used as entered, so apply the same coding as the development data.

Variable reference (49 predictors)

Coding and ranges observed in the development data. Site variables describe the participant's program and were calculated from its participants; programs with fewer than 26 discharged participants had them set to missing.

VariableDescriptionCodingObserved range% recorded