Basic Mapping Pipeline

[josephjclark]

The first mapping service, Aisha's mapping service, is designed to be the simplest form of mapper we can get.

The mapper is designed to generate a single sheet of the lookup tables spreadsheet. To get all the lookup tables needed for the asri-satusehat project, you have to run the mapper multiple times.

It'll work like this:

• input: keys - a list of commcare strings (keys), perhaps with context. As a file, one key per line
• input: vocabs - a target vocabulary (one item per line) to which we want to map. Maybe as a tuple-like system/display/code
• output: a JSON object with commcare keys mapped to fhir objects

And that's it.

The payload needs to be like:

{
  keys: ["Typhoid fever", "Dysentery, bacterial", ...],
  vocab: ["A01.0", "A06.0"]
}

(this example maps to ICD10 codes, not fhir)

We will want to add some script or CLI support to build this payload from files (the openfn CLI could help here)

Design Notes

In this system, do we even need a database and embeddings?? We're asking the human to slice down the dataset for us so we may not even need rag. Having said that, a) If we run the model for each individual mapping, then maybe we still want to use rag even from a small list of inputs, and b) if we build it with rag now, then we can scale better later.

Ok, so we probably do want to use embeddings. But I think we still need to dynamically embed the inputs from the user, rather than pulling from a larger corpus.

This is a tricky detail and key to the whole design.

Mapping Notes

Which sheet do we want to start with? They all have problems...

• diagnosis with body site - there are two things to map: ICD10 codes and snomed body sites in FHIR
• observations - map strings to a category AND a loic code
• option sets - commcare strings to snomed codes

Validation

In Aisha's project specifically, we have the actual answers to the mapping in the spreadsheet.

We should be able to use that information to build formal validations of the results. Like we can literally diff the output and report a success score

Maybe, more generally, if you're doing some mapping and you get some results out and verify them, but then you want to go back and regenerate the mappings because you've added new inputs (or one turned out to be wrong). You might well want to pass your previous result as a validation set to make sure things aren't moving

CLI Integration

I don't know if we want to integrate this into the CLI as part of this work, but I think it's helpful to look ahead and think about what the CLI interface might look like

openfn map-vocab \
	--vocab path/to/file \    # or pre-loaded dataset name
	--inputs path/to/file \
	--prompt "map the inputs to fhir codings like { system, display, code }"

Limitations

Obviously this is a very limited system!

• The inputs must be very carefully curated right now. The human must build very particular lists - and identifying and building those lists is a big part of the problem
• We assume FHIR output, which is a very particular requirement. We'll have explicitly code in support for this (whether in the prompt or whether fill template in pythoin)

The fhir thing isn't quite right is it, because we need to map to ICD10 for diagnosis and FHIR for body sites.

Next Steps

We need to go on to solve the following problems. These are part of the main top level epic really, but I want to be mindful of upcoming problems and possible solutions.

• We need broader inputs and target specs. I don't know what this looks like yet. Commcare XML and all Loinc is too broad, right? Maybe the problem of extracting keys that need mapping in commcare is always a human problem (or, put another way , maybe we need a separate AI service to handle this specific problem, if it comes up enough). So I'm not too worried about that. But I am worried about how the user specifies what targets to map to. Can we slice up loinc like loinc.diseases, and then set the "target dataset" input to loinc.diseases? And if so, how does the user know what datasets are available, and how do they add more?
• Could we use natural language inputs? Like, instead of these formal command line arguments - which probably need some extra context that I haven't captured yet - should we just take plain text? Take these commcare strings and map them to disease codes in the attached loinc dataset, and encode them as fhir codings with a display, system and code. I have two concerns about this: unless you know the pattern, is it too hard to provide the text? And what if key information is missing, or the AI fails to extract it?
• Handling ambiguity. Can the AI return and ask for more information? How do we handle formal errors, like missing data? Can we have any kind of confidence reporting per mapping?

[josephjclark]

Maybe we can ICD10 codings as a first pass? We can just map disease diagnoses and manually cherry-pick inputs.

That gives us an easier pipeline, which we can then go on to extend to use LOINC and SNOWMED

[josephjclark]

Ok, this will really help us slice down the basic mappings.

What we're trying to do here is generate the sheets the lookup-tables xlsx.

Each sheet is tightly contextualised: mapping a list of observations (as commcare strings) to a particular subset of loinc codes.

Our input to this workflow is going to be more like "For this particular commcare form or input, extract the inputs values, and for each of thise, map them to one of these loinc observartions (and by the way, map them as fhir codings in this format)

CC @hanna-paasivirta

[josephjclark]

@hanna-paasivirta I've updated the issue based on yesterdays updates. We'll talk about it this morning.