A Python package for computing cardiovascular disease risk using clinically validated models.
Project description
CVD-risk-scores
A Python package for computing cardiovascular disease risk using clinically validated models.
Note: This is a work in progress
Install
This package is available on PyPI
pip install cvd-risk-scores
Getting started
CVD-risk-scores
is based upon the torch
philosophy of callable objects. Each RiskModel
subclass is a callable upon
initialisation, taking an array-like of subjects, each defined by some variables of interest, and returning an array-like of float
values corresponding to cardiovascular risk scores.
import cvd_risk_scores
# Instantiate the risk model we are interested in
risk_model = cvd_risk_scores.FraminghamRiskScore()
# Define our subject, in this case using a dictionary
subject = {
"gender": "female",
"age": 61,
"SBP_nt": 124,
"SBP_t": 0,
"TotalChol": 180,
"HDL": 47,
"smoker": True,
"diabetes": False
}
# Create a numpy array from features values
data = np.array([list(subject.values())])
# Alternatively:
#data = np.array([
# ["female", 61, 124, 0, 180, 47, True, False]
#])
# define a dictionary mapping our own column names to the names
# expected by the risk score model.
# if data is either a numpy array or a list of lists,
# the `columns_map` mapping must present the columns
# in the correct order so that data can be cast to a pandas DataFrame.
columns_map = {
"gender": "sex",
"age": "age",
"SBP_nt": "SBP_nt",
"SBP_t": "SBP_t",
"TotalChol": "tch",
"HDL": "HDL",
"smoker": "smoking",
"diabetes": "diabetes"
}
# Compute the risk score
risk_score = risk_model(data=data, columns_map=columns_map)
About
CVD-risk-scores
is a Python package for computing cardiovascular disease risk using clinically validated models. It exposes an object-based API for interacting with risk models that is inspired by the torch
paradigm of callable objects.
Available risk models:
- Framingham Risk Score
References
[1] D'Agostino, Ralph B Sr et al. “General cardiovascular risk profile for use in primary care: the Framingham Heart Study.” Circulation vol. 117,6 (2008): 743-53. doi:10.1161/CIRCULATIONAHA.107.699579
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