Application to identify instances of agitation.

Development approach: Machine-learning. Classification of past or present symptom: Both. Classes produced: Positive

The output includes-

Examples of positive mentions:

“Very agitated at present, he was agitated”,

“He was initially calm but then became agitated and started staring and pointing at me towards”,

“Should also include no longer agitated. “

Examples of negative / irrelevant mentions (not included in the output):

“He did not seem distracted or agitated”,

“Not agitated”,

“No evidence of agitation”,

“A common symptom of psychomotor agitation”

Search term(case insensitive): agitat

Cohen's k = 85% (50 un-annotated documents - 25 events/25 attachments, search term ‘agitat*’). Instance level (testing done on 100 random documents): Precision (specificity / accuracy) = 85%

Recall (sensitivity / coverage) = 79% Patient level: All patients with primary diagnosis code F32 or F33 (testing done on 30 random document, one document per patient) Precision (specificity / accuracy) = 82%