HDR UK Gateway
HDR Gateway logo

Bookmarks

Blunted Affect

Description

Application to identify instances of blunted affect

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

The output includes- Positive annotations include:

“his affect remains very blunted”, “objectively flattened affect”, “states that ZZZZZ continues to appear flat in affect”

Exclude Negative annotations:

“incongruent affect”, “stable affect”, “no blunted affect”

Exclude Unknown annotations:

“typical symptoms include blunted affect”, “slightly flat affect”, “relative shows flat affect” Definitions: Search term(s): affect blunt [0 to 2 words in between] *affect flat [0 to 2 words in between] affect restrict [ 0 to 2 words in between affect affect [0 to 2 words in between] blunt affect [0 to 2 words in between] flat Affect [0 to 2 words in between] restrict

Results/Insights

Cohen's k = 100% (50 annotated documents - 25 events/25 attachments). Instance level, i.e. for all specific mentions (testing done on 100 random documents): Precision (specificity / accuracy) = 94% Recall (sensitivity / coverage) = 92% Patient level – Testing done on 30 random document, one document per patient. Precision (specificity / accuracy) = 90%