This algorithm was designed for the vantage6 architecture.
The algorithm expects each data node to hold a csv with the following data
and adhering to the following standard:
{
"id": {
"description": "patient identifier",
"type": "string"
},
"t": {
"description": "patient t stage",
"type": ["integer", "categorical"],
"values": [
[-1, 0, 1, 2, 3, 4],
["Tx", "T1a", "T1b", "T1c", "T2a", "T2b", "T3", "T4"]
]
},
"n": {
"description": "patient n stage",
"type": ["integer", "categorical"],
"values": [[-1, 0, 1, 2, 3], ["Nx", "N0", "N1", "N2", "N3"]]
},
"m": {
"description": "patient m stage",
"type": ["integer", "categorical"],
"values": [[-1, 0, 1], ["Mx", "M0", "M1a", "M1b", "M1c"]]
},
"date_of_diagnosis": {
"description": "date the patient was diagnosed",
"type": "string",
"format": "%Y-%m-%d"
},
"date_of_fu": {
"description": "date the patient had the last follow up visit",
"type": "string",
"format": "%Y-%m-%d"
},
"vital_status": {
"description": "patient vital status",
"type": "categorical",
"values": ["alive", "dead"]
},
}Below you can see an example of how to run the algorithm:
import time
from vantage6.client import Client
# Initialise the client
client = Client('http://127.0.0.1', 5000, '/api')
client.authenticate('username', 'password')
client.setup_encryption(None)
# Define algorithm input
input_ = {
'method': 'master',
'master': True,
'kwargs': {
'org_ids': [2, 3], # organisations to run algorithm
'max_iter': 15, # maximum number of iterations to perform
}
}
# Send the task to the central server
task = client.task.create(
collaboration=1,
organizations=[2, 3],
name='v6-healthai-survival-analysis-py',
image='ghcr.io/maastrichtu-cds/v6-healthai-survival-analysis-py:latest',
description='run nsclc 2-years survival',
input=input_,
data_format='json'
)
# Retrieve the results
task_info = client.task.get(task['id'], include_results=True)
while not task_info.get('complete'):
task_info = client.task.get(task['id'], include_results=True)
time.sleep(1)
result_info = client.result.list(task=task_info['id'])
results = result_info['data'][0]['result']If you wish to test the algorithm locally, you can create a Python virtual environment, using your favourite method, and do the following:
source .venv/bin/activate
pip install -e .
python v6_kmeans_py/example.pyThe algorithm was developed and tested with Python 3.7.
This project was financially supported by the AiNed foundation.
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