DS2 Predicate APIs

The DS2 approach to classifier development, evaluation, and integration involves three steps:

1. Train, evaluate, and select candidate classifiers based on the actual presence or absence of the target condition in test data, using WEKA  – a widely-used, general purpose data mining tool.
2. Experiment with candidate classifiers in the Inference Analyzer – a visual environment custom-developed as part of the DS2 project to present individual patient records and show the results of reducers derived from the classifier-based predicates.
3. Plug classifiers into OpenCDS and the larger Predicate/Reducer architecture in order to use them to help redact conditions from CCDs.

We designed two Application Programming Interfaces (APIs) to connect the classifiers developed in step 1 to the Inference Analyzer and OpenCDS Predicate-Reducer in steps 2 and 3:

• SimpleProbabilisticPredicate – For classifiers that work on one section of the medical record at-a-time, this API passes a simple one-dimensional list of clinical facts, such as a list of problem diagnoses or a list of medications, to the classifier.
• ProbabilisticPredicate – For classifiers that work on the entire patient record, this API passes a vMR object, containing all components of the patient’s medical record, to the classifier.