# Health Metrics Dashboard A tool to upload blood test PDF reports, redact sensitive data, extract metrics via an LLM, and visualize results in a structured health dashboard. ## Purpose Upload one or more blood test PDFs, optionally redact sensitive data, send to an LLM (OpenAI, Anthropic, or Fireworks) for structured extraction, validate the output, and render a multi-category health dashboard with historical comparisons. ## Workflow ### Step 1: Upload PDFs - User uploads one or more blood test PDF reports via a file input or drag-and-drop zone. - Uploaded files are listed with filename and size. - Files are read in the browser (no server upload). ### Step 2: Redact Sensitive Data - PDF text is extracted first (via PDF.js) and shown in a plain-text view. - The user selects text spans to redact; selected spans are replaced with `[REDACTED]` inline. - Keyboard shortcut: press `R` to redact the current selection. - Undo stack allows reverting individual redactions before confirming. - This step is optional — a "Skip" button advances without redacting. - The redacted plain text is what gets sent to the LLM — no PDF rendering or coordinate mapping involved. ### Step 3: Configure Provider - User selects provider: Anthropic, OpenAI, or Fireworks. - Provider selection is persisted to `localStorage` under `health_provider`. - If no API key exists for the selected provider, an inline key entry section appears. - Keys are stored in `localStorage` under `{provider}_api_key`. ### Step 4: LLM Extraction - PDF content (post-redaction) is sent to the selected LLM with the extraction prompt (see below). - The LLM returns a structured JSON object conforming to the metrics schema. - Metrics not in the schema are surfaced separately so the user can consider extending the schema. ### Step 5: Validation - The LLM response is validated against the schema using [Valibot](https://github.com/fabian-hiller/valibot). - Validation errors are shown in the error screen. - Valid entries are accepted and stored in the session. ### Step 6: Dashboard - All validated measurement entries are rendered in the dashboard. - The dashboard builds a historical record across uploads and page loads (see Session & Persistence below). ## Extraction Prompt The prompt instructs the LLM to: 1. Treat the attached document(s) as blood test laboratory reports. 2. Extract every measurable metric it can identify. 3. Return a **JSON object** `{ "entries": [{ ... }] }` with exactly one entry — each file is sent in a separate LLM call. 4. Only include fields where a value was found — omit fields with no data entirely (reduces response size). 5. For each metric found that does **not** map to the schema, return it in a separate `unknown_metrics` array with: `name`, `value`, `unit`, and `raw_text`. 6. Return valid JSON only, no prose. ## Metrics Schema ```json { "type": "object", "properties": { "date": { "type": "string", "format": "date" }, "zinc": { "type": "object", "properties": { "umol_l": { "type": ["number", "null"] } } }, "total_testosterone": { "type": "object", "properties": { "nmol_l": { "type": ["number", "null"] }, "ng_ml": { "type": ["number", "null"] } } }, "free_testosterone": { "type": "object", "properties": { "pmol_l": { "type": ["number", "null"] } } }, "shbg": { "type": "object", "properties": { "nmol_l": { "type": ["number", "null"] } } }, "tsh": { "type": "object", "properties": { "uU_ml": { "type": ["number", "null"] } } }, "free_t4": { "type": "object", "properties": { "ng_dl": { "type": ["number", "null"] }, "pmol_l": { "type": ["number", "null"] } } }, "free_t3": { "type": "object", "properties": { "pg_ml": { "type": ["number", "null"] }, "pmol_l": { "type": ["number", "null"] } } }, "folate": { "type": "object", "properties": { "ng_ml": { "type": ["number", "null"] }, "nmol_l": { "type": ["number", "null"] } } }, "vitamin_b12": { "type": "object", "properties": { "pg_ml": { "type": ["number", "null"] }, "pmol_l": { "type": ["number", "null"] } } }, "vitamin_d_25_oh": { "type": "object", "properties": { "ng_ml": { "type": ["number", "null"] }, "nmol_l": { "type": ["number", "null"] } } }, "cholesterol": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "mmol_l": { "type": ["number", "null"] } } }, "cholesterol_hdl_ratio": { "type": ["number", "null"] }, "hdl": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "mmol_l": { "type": ["number", "null"] } } }, "ldl": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "mmol_l": { "type": ["number", "null"] } } }, "triglycerides": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "mmol_l": { "type": ["number", "null"] } } }, "tg_hdl_ratio": { "type": ["number", "null"] }, "free_androgen_index": { "type": "object", "properties": { "percent": { "type": ["number", "null"] } } }, "luteinizing_hormone": { "type": "object", "properties": { "iu_l": { "type": ["number", "null"] } } }, "fsh": { "type": "object", "properties": { "iu_l": { "type": ["number", "null"] } } }, "potassium": { "type": "object", "properties": { "mmol_l": { "type": ["number", "null"] } } }, "calcium": { "type": "object", "properties": { "mmol_l": { "type": ["number", "null"] } } }, "magnesium": { "type": "object", "properties": { "mmol_l": { "type": ["number", "null"] } } }, "creatinine": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "umol_l": { "type": ["number", "null"] } } }, "gfr": { "type": "object", "properties": { "ml_min": { "type": ["number", "null"] } } }, "bun": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "mmol_l": { "type": ["number", "null"] } } }, "uric_acid": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "umol_l": { "type": ["number", "null"] } } }, "asat_got": { "type": "object", "properties": { "u_l": { "type": ["number", "null"] } } }, "alat_gpt": { "type": "object", "properties": { "u_l": { "type": ["number", "null"] } } }, "gamma_gt": { "type": "object", "properties": { "u_l": { "type": ["number", "null"] } } }, "bilirubin": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "umol_l": { "type": ["number", "null"] } } }, "lipase": { "type": "object", "properties": { "u_l": { "type": ["number", "null"] } } }, "alpha_amylase": { "type": "object", "properties": { "u_l": { "type": ["number", "null"] } } }, "iron": { "type": "object", "properties": { "ug_dl": { "type": ["number", "null"] }, "umol_l": { "type": ["number", "null"] } } }, "transferrin": { "type": "object", "properties": { "g_l": { "type": ["number", "null"] } } }, "transferrin_saturation": { "type": "object", "properties": { "percent": { "type": ["number", "null"] } } }, "ferritin": { "type": "object", "properties": { "ng_ml": { "type": ["number", "null"] } } }, "glucose": { "type": "object", "properties": { "mg_dl": { "type": ["number", "null"] }, "mmol_l": { "type": ["number", "null"] } } }, "total_protein": { "type": "object", "properties": { "g_l": { "type": ["number", "null"] } } }, "albumin": { "type": "object", "properties": { "g_l": { "type": ["number", "null"] } } } }, "required": ["date"] } ``` Unknown metrics returned by the LLM are collected in: ```json { "unknown_metrics": [ { "name": "string", "value": "number|null", "unit": "string|null", "raw_text": "string|null" } ] } ``` These are surfaced to the user with a prompt to consider extending the schema. ## Marker Definitions All markers are defined in a flat `MARKER_DEFS` array. Each entry has: | Field | Description | |-------|-------------| | `key` | Property key in the extracted JSON | | `name` | Display name | | `pu` | Primary unit key (e.g. `nmol_l`); `null` for scalar metrics | | `pl` | Primary unit label (e.g. `nmol/l`) | | `au` | Alt unit key (optional) | | `al` | Alt unit label (optional) | | `ref` | Reference range string for display | | `cat` | Category name | | `check(v)` | Returns `'ok'`, `'borderline'`, `'high'`, `'low'`, or `null` (for missing values) | `CATEGORIES` is derived from `MARKER_DEFS` — it is not declared separately: ```js const CATEGORIES = CATEGORY_NAMES.map(name => ({ name, markers: MARKER_DEFS.filter(m => m.cat === name) })) ``` ### Metric Categories | Category | Metrics | |---|---| | Hormones | Total Testosterone, Free Testosterone, SHBG, Free Androgen Index, LH, FSH | | Thyroid | TSH, Free T4, Free T3 | | Vitamins & Minerals | Zinc, Folate, Vitamin B12, Vitamin D (25-OH), Potassium, Calcium, Magnesium | | Cholesterol & Lipids | Cholesterol, HDL, LDL, Triglycerides, Chol/HDL Ratio, TG:HDL Ratio | | Metabolic & Kidney | Glucose, Creatinine, GFR, BUN, Uric Acid | | Liver Function & Other | ASAT/GOT, ALAT/GPT, Gamma-GT, Bilirubin, Lipase, Alpha-Amylase, Iron, Transferrin, Transferrin Saturation, Ferritin, Total Protein, Albumin | ## Dashboard ### Page Structure (top to bottom) 1. **Header**: title + timespan summary + action buttons (Download CSV, Add report, Clear all) 2. **Markers**: All metrics grouped by category, each with sparkline and expandable history 3. **Timetable**: All measurement dates as columns, markers as rows, with category filter 4. **Key Highlights & Clinical Summary**: Non-OK markers with findings, status badges, and recommendations 5. **Unknown Metrics**: Metrics extracted by LLM but not in the schema 6. **Footer**: Disclaimer — "Not medical advice. Consult a physician for clinical decisions." ### Markers Section Each marker row shows: - Marker name + primary unit label - Inline sparkline SVG (80×24 px) showing trend across all measurement dates - Latest value (colored by status) + alt unit below if available - Reference range (hidden on small screens) - Status badge: `ok`, `~`, `high`, or `low` - Chevron (`▸`/`▾`) indicating expand/collapse state Clicking a row toggles an expand panel showing all historical measurements per date, plus the reference range. Expand state is tracked in a module-level `expandState` object (not a signal), so rows stay open across dashboard re-renders. ### Timetable - daisyUI `table table-xs` with monospace font - First column: marker name + unit; subsequent columns: one per measurement date (formatted as `Mon 'YY`) - Category filter via `