Transform Unstructured Medical Text into Structured Intelligence
Google's revolutionary Python library for extracting precise, structured information from clinical notes, radiology reports, and healthcare documents using advanced AI technology.
LangExtract revolutionizes how healthcare professionals and researchers extract meaningful insights from unstructured clinical texts. Our Google-powered AI solution offers unparalleled accuracy and efficiency in healthcare document processing.
LangExtract provides exact source grounding, mapping every extracted piece of information back to its precise location in the original text. This ensures complete traceability and verification of extracted medical data, crucial for clinical decision-making and regulatory compliance.
Specifically designed for healthcare applications, LangExtract excels at processing clinical notes, radiology reports, pathology results, and discharge summaries. The system understands clinical terminology, abbreviations, and complex healthcare contexts.
Generate beautiful, interactive HTML visualizations of your extracted data. LangExtract creates comprehensive dashboards that help healthcare professionals quickly understand patterns and insights within large volumes of clinical text.
Handle large healthcare document collections with advanced text chunking, parallel processing, and multiple extraction passes. LangExtract efficiently processes thousands of clinical documents while maintaining accuracy and consistency.
Adapt LangExtract to your specific healthcare domain without fine-tuning models. Configure extraction parameters, define custom schemas, and integrate with various LLM providers including Google Gemini for optimal performance.
Built with healthcare security standards in mind, LangExtract ensures HIPAA compliance and data privacy. Process sensitive patient information securely with local deployment options and encrypted data handling.
LangExtract transforms how healthcare organizations process and analyze clinical documents, enabling better patient care through structured data insights.
Extract structured findings, measurements, and diagnoses from radiology reports. LangExtract identifies anatomical locations, abnormalities, and recommendations, converting free-text reports into structured data for analysis and integration with PACS systems.
Transform physician notes into structured clinical data. Extract patient symptoms, treatment plans, medication changes, and clinical assessments from narrative documentation, improving care coordination and clinical research capabilities.
Convert complex pathology reports into standardized formats. LangExtract extracts tumor characteristics, staging information, biomarker results, and diagnostic conclusions, facilitating cancer research and treatment planning.
Structure discharge summaries for continuity of care. Extract admission reasons, procedures performed, medications prescribed, and follow-up instructions, ensuring seamless transitions between healthcare providers.
Accelerate clinical research by extracting relevant data points from patient records. LangExtract identifies patient cohorts, treatment outcomes, and adverse events, supporting evidence-based care and drug development.
Support healthcare quality initiatives by extracting quality metrics from clinical documentation. Identify opportunities for improvement, track care standardization, and measure patient safety indicators.
Extraction Accuracy for Clinical Texts
Faster Than Manual Processing
Healthcare Document Types Supported
Healthcare Organizations Using LangExtract
Start extracting structured information from clinical texts in minutes. LangExtract's simple installation and intuitive API make it easy to integrate into your healthcare workflow.
pip install langextract
import langextract
# Initialize LangExtract for clinical text processing
extractor = langextract.MedicalExtractor(
model="gemini-pro",
domain="clinical_notes"
)
# Extract structured data from clinical text
clinical_text = """
Patient presents with chest pain and shortness of breath.
Physical exam reveals elevated blood pressure 140/90.
Recommended cardiac workup including ECG and troponin levels.
"""
# Define extraction schema
schema = {
"symptoms": ["chest pain", "shortness of breath"],
"vital_signs": {"blood_pressure": "140/90"},
"recommendations": ["cardiac workup", "ECG", "troponin levels"]
}
# Perform extraction
results = extractor.extract(clinical_text, schema)
print(results.structured_data)
print(results.source_mapping)
Get answers to common questions about LangExtract and its applications in healthcare text processing.