This repo has been archived is no longer supported.
You can however use this as a demo to run on your local machine.
To run the demo, download the repo, run the docker-compose.yaml,
and point the browser to localhost:8080:
git clone https://github.com/c4dt/service-spindle
cd service-spindle
docker-compose up -d
open localhost:8080For any questions contact c4dt-dev@listes.epfl.ch.
Machine learning allows computers to make choices without being explicitly programmed for each case. As this field is quite enormous, we will only talk about supervised learning, a special case of machine learning, which happens to be the one shown in the demonstrator.
Supervised machine learning tries to match inputs to outputs, by finding links between both. First, the program is given a list of known links, with that, it creates what is called a "model" of the data, which can be used to determine what would probably be the output based solely on the inputs.
In the Spindle demo, three hospitals want to determine if a patient has diabetes or not. To avoid invasive procedures, they only want to proceed with the full test if it is likely that the patient has it.
To predict if someone has diabetes, these hospitals use what is known as a generalized linear model. It is a statistical method used to predict the likeliness of someone exhibiting a condition, based on a known set of representative values. It works in two phases, first the training, where the model ingests data, then you can predict with the trained model, to determine if some new patient is likely sick. For example, imagine if having diabetes was only determined by the age of the patient, after 40, you are assured to have it. After training, your model will come to the same conclusion, and when asked to predict if a patient is diabetic, it will only look at their age, and answer accordingly.
