Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2

Nationwide Children's Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semistructured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.

OBJECTIVE

We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.

METHODS

We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.

RESULTS

26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.

CONCLUSION

Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.

Clinician, parent, and child prediction of medication or placebo in double-blind depression study

To evaluate how well a blind is maintained in a double-blind study. Clinicians (n = 66), parents (n = 62), and depressed child/adolescent subjects (n = 62) predicted whether the patient had been on either placebo or active medication at the end of an eight-week double-blind placebo versus fluoxetine trial. Clinician, patient and parents' guesses as to which treatment they had received were at a chance level based on an overall analysis. However, when clinical response and condition assignment were controlled, all were correctly predicting placebo treatment but not medication treatment. The finding that subjects, parents and clinicians predict at a chance level is important for double-blind study design integrity. However, clinicians, parents and subjects were accurately predicting placebo treatment when clinical response and the assigned condition were taken into account but not medication. Since they do not know condition however, all remain essentially blinded, and this is an important finding for design and analysis integrity for double-blind studies.