Clinical NLP Analytics Engine/Inference Engine
Advanced Use of Unstructured Clinical Narrative Data
Approximately 80% of clinical information remains largely unutilized in clinical textual notes. Extraction and effective use of information contained in clinical notes using advanced technologies such as clinical natural language processing (cNLP) is often beyond the technical capabilities of many researchers and many institutions. To reduce the technical challenges of performing and utilizing data processed with cNLP, Loyola has developed a new approach to cNLP and its use in a general-purpose clinical inference engine. These technologies are:
- Clinical natural language processing analytics engine. This engine is known as the “clinical natural language processing analytics engine” or “cNAE”. This engine is referred to as an “analytics” engine, as opposed to a natural language processing engine, as the engine has native analytics capabilities that are beyond those found in traditional NLP systems. This engine is high performance in its NLP throughput and output quality. The engine is architected with a native REST application programming interface (API) and optimized to support real-time cNLP needs.
- Clinical natural language processing inference engine. This engine is known as the “clinical natural language processing inference engine” or “cNIE”. This inference engine allows users to take knowledge that they have uncovered or discerned and create inference rules (or a series of inference rules) that utilize this knowledge in real-time to accomplish some analytic process (e.g., a computable phenotype, predictive model, risk model, etc.). Clinical inference rules can have structured and unstructured (e.g., narrative) inputs. This engine is high performance in its throughput and transparently performs cNLP as needed, via the cNAE, during inference rule evaluations. The engine is architected with a native REST application programming interface (API) and optimized to support real-time inference rule processing.
Beyond these two core cNLP engines, Informatics and Clinical Research staff have developed a number of supporting cNLP utilities. The cNLP utility “toolbox” is in testing and will soon be available on all major computing platforms. These cNLP technologies are in use by a number of faculty and access to these resources is by request. If you would like more information on these resources, please use this link Informatics and Clinical Research.
Resource available to the following users:
cNAE/cNIE will be available on a per-request basis to Loyola faculty and students.
Requests for cNAE/cNIE use require:
Requests for cNAE/cNIE use will require a Loyola faculty member as the principal investigator.
Current cNAE/cNIE resources:
CHOIR Health Informatics Seminar Series:
- THE CNAE: NATURAL LANGUAGE PROCESSING FROM THE ELECTRONIC HEALTH RECORD
- THE CNIE: USING NATURAL LANGUAGE PROCESSING FOR INFERENCE OR CLINICAL DECISIONS
Questions or requests for information can be sent to the Informatics and Clinical Research team using this form.