Medical Doc Parser
Diagnosis extraction and ICD-10 coding from medical documents.
The Problem
Medical professionals spend significant time manually extracting diagnoses from documents and coding them to ICD-10 standards. This process is error-prone and delays clinical workflows.
The Solution
An NLP-powered parser that extracts diagnoses from medical documents and automatically maps them to ICD-10 codes. Supports multiple document formats and integrates with clinical systems.
Architecture
%%{init: {'theme': 'dark', 'themeVariables': { 'fontFamily': 'Inter', 'secondaryColor': '#1e293b', 'primaryColor': '#3b82f6', 'primaryBorderColor': '#60a5fa' }}}%%
graph TB
subgraph Input ["Input"]
A["Medical Document<br/>(PDF/scan)"]
end
subgraph Processing ["Processing Pipeline"]
A --> B["OCR Engine<br/>>95% accuracy"]
B --> C["Document Classification"]
C --> D["Diagnosis Extraction<br/>(NLP multilingual)"]
D --> E["ICD-10 Mapping<br/>>90% accuracy"]
E --> F["Anonymization<br/>100% verified"]
end
subgraph Output ["Output"]
F --> G["Structured Data<br/>+ ICD-10 Codes"]
end
classDef default fill:#0f172a,stroke:#334155,color:#fff,stroke-width:1px;
classDef agent fill:#0f172a,stroke:#3b82f6,color:#fff;
classDef process fill:#0f172a,stroke:#334155,color:#fff;
class D,E agent;
class B,C,F process;
AI Agent
Process Step
Tags
TypeScriptNLPICD-10
Outcomes
- >95% OCR accuracy, >90% correct ICD-10 mapping
- Multilingual support: Spanish, Catalan, Galician
- Processing under 2 min/doc, deployed for Tirea