Code for the paper:
Incorporating Machine Learning and Social Determinants of Health Indicators into Prospective Risk Adjustment for Health Plan Payments
Jeremy A. Irvin, Andrew A. Kondrich, Michael Ko, Pranav Rajpurkar, Behzad Haghgoo, Bruce E. Landon, Robert L. Phillips, Stephen Petterson, Andrew Y. Ng, Sanjay Basu
Given demographic and diagnosis information (and optional ZIP code) of a patient, predict prospective annual healthcare spending using a trained LightGBM regression model as described in our paper.
- Please install Anaconda in order to create a Python environment.
- Clone this repo (from the command-line,
git clone git@github.com:stanfordmlgroup/risk-adjustment-ml.git. - Navigate to the cloned repo:
cd risk-adjustment-ml. - Create the environment:
conda env create -f environment.yml. - Activate the environment:
source activate ra-ml.
We format the data in a CSV with columns: Patient (Integer), Age (Integer), Sex (Char: M/F), 5-digit ZIP code (Integer, optional), as well as a list of ICD-10 Diagnoses (Comma-delimited quote string). An optional "Zipcode" column is needed to run the SDH-based model. Each patient should be placed on their own row.
The following is a toy example CSV (see the example):
| Patient | Age | Sex | Zipcode | ICD10 |
|---|---|---|---|---|
| 1 | 46 | M | 95120 | "F32.0,D64.0,D64.81" |
| 2 | 64 | F | 85001 | "L25.0" |
| 3 | 56 | F | 72201 | "R10.0,R10.30,R53.81" |
| 4 | 18 | M | 80201 | "" |
| 5 | 50 | F | 800 | "N15.9,N30.00" |
The script can be run using Python with the following flags:
python run.py --csv_path path/to/data --model_path path/to/saved/model --save_path path/to/save/output
Including --sdh will automatically run the SDH-based model. This requires ZIP code data for each patient row. For example, to run the SDH-based model on the demo CSV,
python run.py --csv_path demo.csv --model_path ml_model_sdh.txt --save_path costs.csv --sdh