Get pomegranate installed in Python3:
pip3 install pomegranate
Or get the latest version of pomegranate
git clone https://github.com/jmschrei/pomegranate cd pomegranate python setup.py install
Please refer to https://pgmpy.org/models/bayesiannetwork.html for more tutorials on creating a Bayesian network
This is the third part of the Hack$rare solution Trackpain, an inference model that can help predict pain levels for non-communicative children. Please refer to this page for the complete solution.
To show a proof of concept, we use an imaginary person whose biological data and pain scales are recorded. We will show how to construct a network for this particular individual patient.
- The sensor will collect biological data including heart rate variability, respiration rate, actigraphy, and skin conductance
- The phone app will collect self-reported metrics obtained from visual pain analog, including facial and emotional scores; the third party caregivers can also make observations about eating, sleeping, social, and verbal patterns.
- Refer to this notebook for data cleaning probabilty calculation.
- Given probabilities value, create a static binary discrete Bayesian network
- Refer to this notebook for model creation and prediction
- Make a continuous instead of discrete network
- following this pipeline, construct a dynamic network that fits all patients data
- Note that we are trying to solve the error in network creation. See Issue Section.
- We've updated the model so it can make predictions now! Please check this link: https://colab.research.google.com/drive/1N4vU16H4AUoZejXxH6Op-ekH4pLF4KF3?authuser=1#scrollTo=GIaJfuIqr0E5