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FHIR_HL7_Integrator t1_j0z41ws wrote

I work as an interop architect in healthcare. Payer,provider, device, and emr/ehr side. This is of interest to me. I will read your paper and try your demo and give you my thoughts.

I read your abstract but couldn't read the whole thing ARXIV access isn't working for me this morning. I tested the app and i believe the timeline view is listing a chronological list of encounters (visits) and symptoms and then provides in the box below predict a listing of predicted potential morbidities. I understand this is a POC app so its doesn't really show a ton of details.

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My question is how are you getting at tis data? Is it a database ETL from the EHR? Are you capturing incoming messages (HL7, CDA, FHIR, etc.)? I am working on a project for spontaneous building of healthcare communication between different healthcare entities - as it stands right now we have to manually address data links. I want AI to translate between entities.

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Anyway, fantastic job and congratulations. I hope you have continued success!

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w_is_h OP t1_j0z8x15 wrote

I can send the paper if needed. Regarding the timeline view - you are exactly right, and yes this is just a quick demo, new features will be coming out in the following months (this is a research tool, nothing commercial).

We take all free text from a hospital EHR (done using CogStack a data harmonization platform for hospitals) and extract disorders, symptoms, medications and all relevant biomedical concepts using MedCAT. Then create the timelines, enrich them with any structured data we might have access to and train the models.

Thank you for the feedback.

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FHIR_HL7_Integrator t1_j0z9l0i wrote

Would love to read the paper. If I DM my email would you be ok with that? Thanks

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w_is_h OP t1_j0z9x5k wrote

Of course, please go ahead.

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persistentrobot t1_j128d70 wrote

You should take a look at UniHPF. They make minimal assumptions on data format/mapping by chucking everything into a large language model. It's comparable to performance on FHIR embeddings. I think this is an interesting avenue for machine learning in health, but the failures of large language models are difficult to uncover. Like how much can covariates shift before a prediction flips? Or is the act of measuring the covariate the only information we need?

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