lab-result-interpretation

name lab-result-interpretation description Transforms biochemical lab test results into clear, patient-friendly explanations. Covers blood routine, lipid panel, liver/kidney function, thyroid, electrolytes, and inflammation markers. Flags critical values, classifies severity, and generates structured interpretation rep... license MIT author AIPOCH Source : https://github.com/aipoch/medical-research-skills Lab Result Interpretation Skill A medical assistant tool that transforms complex biochemical laboratory test results into clear, patient-friendly explanations. Quick Check Use this command to verify that the packaged script entry point can be parsed before deeper execution. python -m py_compile scripts/main.py Audit-Ready Commands Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths. python -m py_compile scripts/main.py python scripts/main.py -- help When to Use Interpreting biochemical laboratory test results for patients Generating patient-friendly explanations of abnormal lab values Flagging critical values requiring immediate medical attention Creating structured lab result summary reports Workflow Parse lab report — Input: lab result text or file (--file/--input) → extract test names, values, units, reference ranges using regex patterns → Output: structured test data array Compare to reference ranges — Match each test against references/lab_reference_ranges.json → determine status (normal/high/low) → Output: status classification per test Assess severity — Classify: mild (slightly outside range), moderate (clinically significant deviation), critical (requires immediate attention) → Output: severity rating per abnormal value Generate explanations — For each abnormal value: explain what the test measures, what the deviation means, contextual health information → ⛔ Checkpoint: Flag critical values to user with "Seek immediate medical attention" warning before continuing → Output: patient-friendly explanation per test Format output — Combine all results into structured JSON with test_name, value, status, explanation, severity, recommendation → include medical disclaimer → Output: final interpretation report Features Parses various lab test formats (numeric values, units, reference ranges) Compares values against standard reference ranges Generates patient-friendly explanations in Chinese Flags abnormal values with severity indicators Provides contextual health recommendations Supported Test Types Category Tests Blood Routine WBC, RBC, Hemoglobin, Platelets, Hematocrit Lipid Panel Total Cholesterol, LDL, HDL, Triglycerides Liver Function ALT, AST, ALP, GGT, Bilirubin, Total Protein, Albumin Kidney Function Creatinine, BUN, eGFR, Uric Acid Blood Sugar Fasting Glucose, HbA1c Thyroid TSH, T3, T4, FT3, FT4 Electrolytes Sodium, Potassium, Chloride, Calcium, Magnesium Inflammation CRP, ESR Usage As Module from scripts.main import LabResultInterpreter interpreter = LabResultInterpreter() result = interpreter.interpret( "Total Cholesterol: 5.8 mmol/L (Reference: 3.1-5.7)" ) print (result.explanation) CLI python scripts/main.py --file lab_report.txt python scripts/main.py --interactive Parameters Name Type Default Required Description file string "" No Path to lab report file to process interactive boolean false No Enable interactive mode for manual input input string "" No Direct lab test input string for interpretation Input Format Accepts flexible formats: Test Name: Value Unit (Reference: Min-Max) Test Name Value Unit Ref: Min-Max Test Name: Value (Min-Max) Output Format { "test_name" : "Total Cholesterol" , "value" : 5.8 , "unit" : "mmol/L" , "reference_min" : 3.1 , "reference_max" : 5.7 , "status" : "high" , "explanation" : "Your total cholesterol is slightly above the normal range..." , "severity" : "mild" , "recommendation" : "Consider reducing saturated fat intake..." } Technical Details Difficulty: Medium Key Components: Lab value parsing with regex patterns Reference range comparison logic Medical knowledge base (references/lab_reference_ranges.json) Patient-friendly explanation templates Safety: Includes medical disclaimer in all outputs Flags values requiring immediate medical attention Does not diagnose - only explains test meanings References references/lab_reference_ranges.json - Standard reference ranges references/explanation_templates.json - Patient-friendly templates references/test_metadata.json - Test descriptions and clinical notes Medical Disclaimer This tool provides educational information only and is not a substitute for professional medical advice, diagnosis, or treatment. Always consult with a qualified healthcare provider for interpretation of lab results. Risk Assessment Risk Indicator Assessment Level Code Execution Python/R scripts executed locally Medium Network Access No external API calls Low File System Access Read input files, write output files Medium Instruction Tampering Standard prompt guidelines Low Data Exposure Output files saved to workspace Low Security Checklist No hardcoded credentials or API keys No unauthorized file system access (../) Output does not expose sensitive information Prompt injection protections in place Input file paths validated (no ../ traversal) Output directory restricted to workspace Script execution in sandboxed environment Error messages sanitized (no stack traces exposed) Dependencies audited Prerequisites # Python dependencies pip install -r requirements.txt Evaluation Criteria Success Metrics Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable Test Cases Basic Functionality : Standard input → Expected output Edge Case : Invalid input → Graceful error handling Performance : Large dataset → Acceptable processing time Lifecycle Status Current Stage : Draft Next Review Date : 2026-03-06 Known Issues : None Planned Improvements : Performance optimization Additional feature support Output Requirements Every final response should make these items explicit when they are relevant: Objective or requested deliverable Inputs used and assumptions introduced Workflow or decision path Core result, recommendation, or artifact Constraints, risks, caveats, or validation needs Unresolved items and next-step checks Error Handling If required inputs are missing, state exactly which fields are missing and request only the minimum additional information. If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment. If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback. Do not fabricate files, citations, data, search results, or execution outcomes. Input Validation This skill accepts requests that match the documented purpose of lab-result-interpretation and include enough context to complete the workflow safely. Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond: lab-result-interpretation only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill. Response Template Use the following fixed structure for non-trivial requests: Objective Inputs Received Assumptions Workflow Deliverable Risks and Limits Next Checks If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.