Paper Summary
Read this paper in prep for our manuscript. Seems like we're on a similar path.
· Australia is like an entire SEER site (but it’s not SEER) where you have to report certain cancers to the government. · Even though pathology labs do electronic reporting, the current reporting process to the government is done on paper. This is dumb, so can we automate this? · The first step (corpus selction) is to identify notifiable reports. These are all cytology and histology reports excluding urine, sputum, and pap smear. This is done with a query on, I think, the HL7 data. · The second step is to identify the histology type to see if it is a cancer notifiable result. This step itself has two steps. · Step 2a (NER) is to go over all the SNOMED CT concepts and reason over them (so rule based) to see if they are descendents of one of the notifiable concepts, of which there are six. They pick the most advanced of the concepts from the report as the concept for the report. · Step 2b (status annotation) is to mark each of the concepts that fit the notifiable criteria as absent, possible, or present. · If any notifiable histologies are present and aren’t BCC or SCC of skin then the result is notifiable. · Then there’s some discussion about supporting reports which makes no sense. · “The ground truth was created based on an adjudication process between the reference data set provided by a domain expert and the output of the system for all reports in the development and evaluation set.” · Somehow their corpora ended up having roughly equal number notifiable and non-notifiable reports, which seems crazy to me. · They report sensitivity, PPV, specificity and F-score. · They have 30 misclassifications over both training and test and report an ”error rate” based on this. · They have 7 false negatives and 23 false positives, and report the false negatives are more costly. · The false negatives are mostly due to sectioning and status annotation errors.
