Machine Learning in Healthcare: Now for Everyone
Healthcare.ai is a community with education and open source technology tools focused on increasing the national adoption of machine learning in healthcare
Healthcare.ai has been used to:
- Drive $1M plus in annual savings realized by eliminating an outsourced service line reporting solution.
- Achieve 50% reduction in central line-associated blood stream infection (CLABSI) rates at a large academic medical center.
- Improve self-pay collections using intelligent workflows across more than 150K patients per month.
- Produce literature-beating models across readmissions, infection, and finance—helping clinicians and operations to prioritize resources.
Learn and Engage with the Data Scientists Behind Healthcare.ai
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What can I do with healthcare.ai?
- Create and compare models based on your data.
- Save and deploy a model.
- Perform risk-adjusted comparisons.
- Do trend analysis following Nelson rules.
- Improve sparse data via longitudinal imputation.
- Fill in missing data via imputation.
- Deploy a model to produce daily predictions.
- Write predictions back to a database.
- Learn what factors drive each prediction.
How is it tailored to healthcare?
- Longitudinal machine learning via mixed models.
- Longitudinal imputation.
- Risk-adjusted comparisons.
Our goal with this project is to expedite adoption of ML in healthcare by building pragmatic world class tools to help anyone with access to healthcare data.
You can help in many ways:
- Try out the packages and let us know what needs improvement!
- Check out our Github repos
How do I get started?
Healthcare.ai is available in packages for both R and Python, two of the most common languages used by data scientists. If you don’t previous experience with either language, we recommend the R package as it currently has more features and R is more newbie-friendly.
How does healthcare.ai focus on healthcare?Both packages differ from other machine learning packages in that they focus on data issues specific to healthcare. This means that we pay attention to longitudinal questions, offer an easy way to do risk-adjusted comparisons, and provide easy connections and deployment to databases.
Who is healthcare.ai designed for?While data scientists in healthcare will likely find these packages valuable, the audience healthcare.ai targets are those analysts, BI developers, and SQL developers that would love to create appropriate and accurate models with healthcare data.
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