[markolschesky]

If you want to play with this and you're looking for a decent sample exam note, I grabbed some pieces of standard looking notes (Physical, ROS, Hand exam) and tossed them into a gist here: https://gist.github.com/molsches/32fcec2499e95b5b23bc268800e...

Mostly impressive in how it parses the data and can find conditions and tests in the unstructured data. Handles a few things strangely, but I imagine that gets better over time as it continues to be trained.

I think for it to truly be useful it needs some layer of semantic data mapping to something to standards like IMO/SNOMED/LOINC/RxNorm etc but I could see that being where other companies build their "products" on top of AWS vs. AWS competing with other Healthcare ML vendors in the space.

[moflome]

Thanks for this sample. And for those looking for the AWS service, there is a walk-through [0] of the Amazon Comprehend NLP service online.

[0] https://aws.amazon.com/blogs/aws/amazon-comprehend-medical-n...

[carbocation]

Did you grab several pieces from several notes? This looks like a garbled mess to me, but if I understood how you had transformed it to get it into this state, it might make more sense.

[nkrumm]

The posted text is actually a series of "dot phrases" or "smart texts" (aka templates in EHR speak), _not_ actual notes. Mostly these are inserted into clinical notes to achieve a certain level of documentation; typically they are saved in a "all negative" format, and the relevant parts are edited to reflect the patient history and physical. These seem to have been derived from the Univ of Washington Emergency Medicine residency.

These might make OK material for some initial testing but they don't reflect a real clinical note. A good source of those might be the MIMIC database [1]

[1] https://mimic.physionet.org/