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Treatment- Resistant Depression

Description

Application to identify instances of treatment-resistant depression.

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

The output includes- Positive mentions:

“X year history of treatment resistant depression”, “problems with low mood (resistant depression)”, “diagnosis: treatment resistant depression”, “resistant endogenous depression, suffers from chronic treatment resistant depression”, “referred for management of treatment resistant recurrent depression”

Exclude Unknown mentions:

“talked about ways in which they might resist allowing each other’s depression to …”, “has a diagnosis of treatment resistant schizophrenia and depression”, “we discussed him enrolling for a study of treatment resistant depression”, “we talked about medication for treatment resistant depression”, “resisted antidepressant therapy for a number of years”, “needs an assessment to rule out treatment resistant depression”, “assess whether depression was resistant to mirtazapine”, “accepts that ECT is a strategy for treatment resistant depression”

Definitions: Search term(s): depression [0-8 words in between] resist resist [0-8 words in between] depression

Results/Insights

Cohen's k = 85% (50 un-annotated documents - 25 events/25 attachments, search term ‘resistant depression’). Instance level , Random sample of 100 Random Documents: Precision (specificity / accuracy) = 77% Recall (sensitivity / coverage) = 95% Patient level , All patients with primary diagnosis code of F32 or F33 in a structured field (Random sample of 50 Random Documents) Precision (specificity / accuracy) = 90%