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Apathy

Description

Application to extract the presence of apathy

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

The output includes- Positive mentions includes:

any indication that apathy was being reported as a symptom: “ continues to demonstrate apathy” “ some degree of apathy noted” “presentation with apathy” “his report of apathy given”.

Exclude Negative mentions of apathy:

“denied apathy” “no evidence of apathy”

Exclude ‘Unknown’ annotations of apathy:

“may develop apathy or as a possible side effect of medication” “apathy found in quite a few names” Definitions: Search term(s): apath

Results/Insights

Cohen's k=86% (50 un-annotated documents - 25 events/25 attachments, search term ‘apath’). Instance level, i.e. for all specific mentions (testing done on 100 random documents): Precision (specificity / accuracy) = 93% Recall (sensitivity / coverage) = 86% Patient level – All patients with primary diagnosis code F32 or F33* (testing done on 30 random document, one document per patient): Precision (specificity / accuracy) = 73%