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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%
Details
License
Not specified
Last Updated
09/06/2025
Associated Authors
CRIS
Datasets that the analysis, tool or software can be applied to (1)
South London and Maudsley NHS Foundation Trust (SLaM) Clinical Record Interactive Search (CRIS) plat
Dataset population size: Unknown
Health and disease