How Standardization Works

Before copying a curl example, choose your Cloud region. For authenticated calls, it must be the same region as your key. The examples below use MIROBODY_API_BASE:

export MIROBODY_API_BASE="https://api.mirobody.ai/v1"   # Global account
# export MIROBODY_API_BASE="https://api.mirobody.cn/v1" # China account: use this instead

For SDK examples with a literal Global URL, use the matching China URL when your account is in China. See Regions. Health data arrives messy — “血糖(空腹)”, “FBG”, “Glucose, fasting” all name the same indicator, and the units vary just as much. Mirobody standardizes every structured reading written through POST /v1/data or stored by POST /v1/standardize, so the agent and your queries see one coherent, coded dataset. POST /v1/files is a separate storage and text-extraction surface; uploading a file standardizes its report values into structured readings automatically. /v1/standardize is the explicit path for that same extraction — use it to inspect the result (dry-run), or to standardize text / file_key sources on demand.

The pipeline

Both a document and a structured record enter the same pipeline:

Read the source

PDF / image → text (OCR); Excel → rows.

Pull out readings

Find each {indicator, value, unit, date}. Documents only — a /v1/data record is already structured and skips straight to coding.

Code the indicator

Match the name to a LOINC code. With no confident match, loinc_code stays null — never a wrong code.

Normalize the unit

Fold the unit spelling to one UCUM form and parse the value to a number: "mg/dl" → mg/dL.

Store as one series

One row per reading on the Subject’s timeline, queryable by indicator, code and date.

The important part is step 3: codes are matched, never invented. A model is great at reading a document but shouldn’t be trusted to recite a code system — a wrong LOINC code is worse than none. So when the match isn’t confident, Mirobody keeps the raw name and leaves loinc_code null rather than guess. When self-hosting, the engine runs the same stages on your own infrastructure. How a reading passes through them, from the device connection to the answer, is described in The Pipeline.

Before / after

What you send to /v1/data (or what /v1/standardize reads from a report) vs. what the structured store holds:

	Before (as written)	After (standardized)
Indicator	`"血糖(空腹)"` / `"FBG"` / `"Glucose, fasting"`	`loinc_code: "1558-6"`, `canonical_name: "Fasting glucose [Mass/volume] in Serum or Plasma"`
Value	`"97 mg/dL"` (one string)	`value: "97"` (raw value, no unit), `parsed_value: "97"`, `parsed_unit: "mg/dL"` (UCUM)
Unit spelling	`"mg/dl"`, `"MG/DL"`, `"mg/dL"`	`"mg/dL"` (one UCUM form)
Interop	free text	LOINC code + UCUM unit, the vocabularies FHIR itself uses
Original	—	kept: `value` holds the raw value as written (e.g. `97`, no unit appended — the unit rides in `parsed_unit`); raw names/units preserved

Three spellings of the same test become one series — trend queries, the agent’s data tools, and your own analytics all see it as one indicator.

Standardized output in the API

Surface	What appears
`POST /v1/data`	Response counts `standardized` alongside `ingested`.
`GET /v1/data`	Every row carries `parsed_value` / `parsed_unit` / `loinc_code` / `canonical_name`, plus the reference range and `abnormal` flag when the source carried them.
`POST /v1/standardize`	The whole pipeline as a synchronous call — dry-run by default, so you can inspect standardization before committing a write.
The agent	Grounded answers query the standardized series — which is why “how’s my glucose?” finds records written as “FBG”.

Cookbook: one call, report → structured data

# Inspect first (nothing persisted) …
curl "$MIROBODY_API_BASE/standardize" \
  -H "Authorization: Bearer $MIROBODY_API_KEY" \
  -F "user=alice" -F "file=@lab_report.pdf"

# … then commit the same extraction
curl "$MIROBODY_API_BASE/standardize" \
  -H "Authorization: Bearer $MIROBODY_API_KEY" \
  -F "user=alice" -F "file=@lab_report.pdf" \
  -F "store=true" -F "retention=permanent"

See Standardize a report for the full row shape (indicator_raw, loinc_code, confidence, …).

​The pipeline

​Before / after

​Standardized output in the API

​Cookbook: one call, report → structured data

​See also

The pipeline

Before / after

Standardized output in the API

Cookbook: one call, report → structured data

See also