extracting-lab-tables

# Extracting lab tables from documents and scans Lab results arrive as **tables**: a column of test names, a value column, units, a reference range, and an abnormal flag (H/L/Crit). To use them downstream you must recover that grid from a PDF, scan, or spreadsheet into clean rows — then code each test to **LOINC**, normalize units with **UCUM**, and flag abnormals. This skill is the **intake** step: it OCRs/parses the table on-device with `openmed.multimodal`, de-identifies any embedded PHI, and emits structured rows. It pairs **before** OpenMed's clinical helpers — the LOINC/UCUM coding and the high/low/critical flag are downstream (see `parsing-lab-values`). ## When to use - You have a lab report as a **scanned image / photo / PDF page** and need the panel as rows, not pixels. - The source is a **CSV/TSV** export and you need columns classified (which is the value, the unit, the range, the flag) and PHI columns redacted. - You need machine-readable rows to feed LOINC mapping and a FHIR `Observation`/`DiagnosticReport`. ## What OpenMed gives you here `openmed.multimodal` ships the intake primitives (no heavy deps at import; the OCR backend loads lazily): - `openmed.multimodal.ocr.ocr(image, engine=...)` → an `OcrResult` whose `.words` are `OcrWord(text, bbox, confidence, page)` and `.text` is the joined string. `OcrResult.to_document()` bridges each word (with its pixel bbox) into an `ExtractedDocument` so detected PHI can project back to the source location. - `read_table(...)` → a `TableView` (`headers`, `rows`, `delimiter`, `has_header`, `columns`) for delimited text; `classify_columns(...)` labels each column; `redact_table(...)` → a `RedactedTable` with a PHI-safe `manifest`. Engines: Tesseract (`pip install "openmed[multimodal]"` + the system binary) or PaddleOCR (`pip install "openmed[ocr-paddle]"`). `ocr()` auto-selects the first installed backend. ## Quick start ```python from openmed.multimodal.ocr import ocr from openmed.multimodal import read_table, classify_columns, redact_table # A) Scanned / image lab report -> words with pixel boxes. result = ocr("cbc_report.png") # OcrResult for w in result.words[:5]: print(repr(w.text), w.bbox, round(w.confidence, 2), "p", w.page) doc = result.to_document() # ExtractedDocument; bbox preserved # B) Delimited lab export (CSV/TSV) -> classified, PHI-redacted rows. csv_text = ( "PatientName,Test,Value,Unit,RefRange,Flag\n" "Jane Roe,Hemoglobin,9.1,g/dL,12.0-15.5,L\n" "Jane Roe,Glucose,148,mg/dL,70-99,H\n" ) view = read_table(csv_text) # TableView view = classify_columns(view) # tag PHI vs data columns redacted = redact_table(view) # RedactedTable: PatientName redacted for row in redacted.rows: print(row) # name column masked; lab data intact for col in redacted.manifest: # PHI-safe per-column audit manifest print(col["column_name"], col["assigned_class"], col["action"]) ``` For an OCR'd (image) table, you reconstruct the grid yourself from word boxes (next section) — OCR yields positioned words, not a delimited table. ## Workflow 1. **Detect the source type.** CSV/TSV → `read_table`. Image/scan → `ocr()`. PDF/DOCX are **not** directly parseable (they raise `UnsupportedDocumentError`); render PDF pages to images first, or extract their text layer, then OCR. 2. **OCR with positions.** `ocr()` returns `OcrWord`s carrying `bbox` and `page`. Keep the boxes — they let you cluster words into rows/columns and project PHI redaction back to pixels. 3. **Reconstruct the grid.** Cluster words by their `bbox` *y* into rows, by *x* into columns. The header row names the columns; align body cells to those x bands. Confidence (`OcrWord.confidence`) flags shaky cells for review. 4. **Identify the lab columns.** Map headers to roles: *test name*, *value*, *unit*, *reference range*, *flag*. For delimited input, `classify_columns` tags PHI columns (name/MRN/DOB) so `redact_table` masks them. 5. **De-identify embedded PHI.** Patient name/MRN often sit in the table header or a leading column. Redact those columns (`redact_table`) and run free-text cells through `openmed.deidentify` before the rows leave the device. 6. **Emit structured rows** `{test, value, unit, ref_range, flag}` per result and hand off to LOINC/UCUM coding and `parsing-lab-values`. ## Hand-off to / from OpenMed - **To** `parsing-lab-values` (`openmed.clinical.parse_reference_range`, `derive_abnormal_flag`): pass the parsed value + `ref_range` (+ any explicit lab flag) to get a structured low/normal/high/critical signal. - **To** `mapping-loinc`: code each test name to a LOINC code; normalize the unit with UCUM. OpenMed emits the row; the terminology binding is out-of-process. - **To** FHIR (`exporting-to-fhir`): each row becomes an `Observation` (`code`=LOINC, `valueQuantity` with UCUM `unit`, `referenceRange`, `interpretation`) grouped under a `DiagnosticReport`. - **De-identify** with `deidentifying-clinical-text` (`openmed.deidentify`) before export. OCR words carry pixel boxes so redaction maps back to the image. - Everything here runs **on-device**; no scan or row leaves the process un-de-identified. ## Edge cases & gotchas - **PDF/DOCX raise `UnsupportedDocumentError`.** The multimodal dispatcher has no PDF/DOCX handler — rasterize PDF pages to PNG (or pull the text layer) before calling `ocr()`. Image formats (PNG/JPG/TIFF/…) and CSV/TSV are handled. - **OCR returns words, not a table.** You must reconstruct rows/columns from `bbox` geometry. Multi-line cells, wrapped test names, and merged header cells break naive x/y bucketing — tune the clustering tolerance per template. - **Reference ranges are easy to mis-split.** "12.0-15.5", "<5", "70 - 99", and en/em dashes must survive OCR and tokenization as one cell. Don't let a space or a misread dash fracture the range — `parse_reference_range` downstream expects it whole. - **Units belong to the value, not the range.** Keep "9.1 g/dL" and the range "12.0-15.5" in separate fields; the value's unit must match the range's unit or the abnormal flag will be wrong (the flag helper is unit-agnostic). - **Low-confidence cells.** Gate on `OcrWord.confidence`; a 0.4-confidence value in a lab table is a patient-safety risk — route it to human review, don't silently accept it. - **PHI hides in tables.** Patient name, MRN, DOB, and accession numbers commonly occupy the header or first column. Classify and redact them; never log the raw table. - **Engine availability.** `ocr()` raises a clear `MissingDependencyError` if no backend is installed — install Tesseract or PaddleOCR per the extras. ## Standards & references - LOINC — universal lab observation codes: https://loinc.org/ - UCUM — Unified Code for Units of Measure: https://ucum.org/ - HL7 FHIR R4 Observation (`valueQuantity`, `referenceRange`, `interpretation`): https://hl7.org/fhir/R4/observation.html - HL7 FHIR R4 DiagnosticReport (lab grouping): https://hl7.org/fhir/R4/diagnosticreport.html - Tesseract OCR: https://github.com/tesseract-ocr/tesseract - PaddleOCR: https://github.com/PaddlePaddle/PaddleOCR - OpenMed source: `openmed/multimodal/ocr.py`, `openmed/multimodal/tabular_csv.py`.