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Diagnosis

Description

Application to extract instances of diagnosis.

Development approach: Rule-Based Classification of past or present symptom: Both.

The output includes- The main aim is to look for a standard or as close as possible to a definitive standard diagnosis:

1.) When reading through document, if you come across phrase(s) similar to the examples below:

……Diagnosis: Fxx.x diagnosis name……(this could be with or without the colon, or could even have several colons and/or other punctuation marks before they diagnosis name, following each

……Diagnosis Fxx.x diagnosis name……

……Diagnosis: diagnosis name……

……Diagnosis: Fxx.x……

Highlight this as ‘Diagnosis’ – please label the annotation just as I have specified it (i.e. with a CAPITAL D).

2.) The following features have been added under the Diagnosis annotation:

ICD10: if there is a name of a diagnosis, but no ICD10 code, find the ICD10 code and fill in under the feature ICD10

Diagname: if there is a diagnosis name then please copy this in the annotation feature. Please copy the exact diagnosis name even if it varies from the official ICD10 name.

Diffdiag – add this only if there is a differential diagnosis. This kind of diagnosis is often mentioned because usually most documents are trying to find out what the diagnosis is and in the process give a possible diagnosis which is vague or will not be the correct one eventually.

See website for remaining definitions and search terms.

Results/Insights

Cohen's k = xx Instance level (Lifetime), Sample of 50 Random Documents:

Precision (F20/ Schizophrenia) – 96%

Recall (F20/ Schizophrenia) – 63%

Precision (F20) - 100%

Recall (F20) - 65%

Precision (SMI) - 95%

Recall (SMI) - 43%

Precision (Schizoaffective) - 80%

Recall (Schizoaffective) - 29%

Precision (Depression) - 100%

Recall (Depression) - 40%

Precision(All patients with primary diagnosis of learning disability (f7 or learning dis*) in a structured field or unstructured text) – 93%