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Bad Dreams

Description

Application to identify instances of experiencing a bad dream

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

The output includes- Positive annotations include:

“ZZZZZ had a bad dream last night”, “she frequently has bad dreams”, “ZZZZZ has suffered from bad dreams in the past”, “ZZZZZ had a bad dream that she was underwater”, “ he’s been having fewer bad dreams”

Exclude Negative mentions:

“she denied any bad dreams”, “does not suffer from bad dreams”, “no other PTSD symptoms such as bad dreams”, “he said the experience was like a bad dream”, “ZZZZZ compared his time in hospital to a bad dream”

Exclude Unknown mentions:

“she said it might have been a bad dream”, “he woke up in a start, as if waking from a bad dream”, “ZZZZZ couldn’t remember whether the conversation was just a bad dream”, “doesn’t want to have bad dreams” Definitions: Search term(s): bad dream*

Results/Insights

Cohen's k = 100% (100 unannotated documents- 50 events/50 attachments, search terms ‘dream’ and ‘dreams’). Instance level, i.e. for all specific mentions (testing done on 100 random documents): Precision (specificity / accuracy) = 89% Recall (sensitivity / coverage) = 100%