Opal: Machine learning dashboard for anesthesia
ABOUT THE PROJECT
Project Description
This is a full pipeline infrastructure that has pre-written queries for pulling patient data from clarity in an organized manner: stores the data in a postgres database on a secure server; allows for php cohort selection of the patients of interest from the entire set of operations done at UCSF you can choose patients by gender, age, type of surgery, preoperative labs, past medical history, etc; and presents the data on javascript browser for data visualization using D3js and machine learning analysis. Physicians can interact and complete complex machine learning analysis without knowing how to program.
Funding
Not available
LOOKING FOR
Required Skills
- Python
- SQL
- TensorFlow
- PyTorch
- MLlib / machine learning tools
- Javascript
None of the above skills are required, but expertise with any one of them is useful
Acceptable Level of Education (eg. Undergrad, Grad Students, Post Docs, MD, PhD)
Undergraduate students, Graduate students, Postgraduates, Full-time workers who volunteer time
Additional Considerations
We plan for this to grow extensively to also be applied to other areas of medicine outside anesthesia.
CONTACT
PI/Research group
Atul Butte
Contact
Andrew Bishara, [email protected]