We are continually developing tools to enable wider and deeper clinical data exploration

Best of breed tools, including UC Berkeley-developed Spark computing paradigm, employing a “compute to the data” model for Very Large data mining and pattern discovery, clinical Text Analysis Knowledge Extraction System (cTAKES), imaging exploration (a.k.a. Imaging Commons), machine learning models, and user friendly interactive interfaces for patient cohort exploration have been implemented.  Additional tools are planned to be added in the future, including Omics Data exploration (a.k.a. Omics Commons), and new clinical text processing tools as they become available.

Tools for Novice Users (no technical training required)

  • PatientExploreR
    User friendly tool that enables patient level interactive dynamic reports and visualization of clinical data without programming skills.
  • CTAKES As-A-Service
    Natural language processing system for extraction of information from electronic medical record clinical free-text.  Originally developed by Mayo Clinic, and now part of Apache products.
  • EMERSE (Electronic Medical Record Search Engine)
    Enables users to search clinical notes through.  Developed at University of Michigan, this tool will be available at UCSF soon.
  • UCSFPhilter

    User-Friendly De-Identification of Clinical Text

Tools for Highly Technical Users

  • Jupyter Notebook
    Open source notebook for sharing documents with live code, equations, visualizations and narrative text.