Skip to content

Changelog

All notable changes to pyCTAKES will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

  • Comprehensive API documentation with mkdocstrings
  • Advanced topics documentation (custom annotators, performance tuning, UMLS integration)
  • Development and contributing guidelines
  • GitHub Actions workflow for documentation deployment

Changed

  • Enhanced mkdocs configuration with material theme
  • Improved navigation structure for documentation

[1.0.0] - 2025-01-15

Added

  • Core pipeline architecture with configurable annotator chains
  • Clinical text processing annotators:
  • ClinicalSentenceSegmenter - Clinical-aware sentence segmentation
  • ClinicalTokenizer - Advanced tokenization with POS tagging
  • ClinicalSectionAnnotator - Clinical document section detection
  • Named entity recognition:
  • ClinicalNERAnnotator - Hybrid rule-based and model-based NER
  • SimpleClinicalNER - Fast pattern-based entity recognition
  • Assertion and negation detection:
  • NegationAssertionAnnotator - pyConText-style context detection
  • UMLS concept mapping:
  • UMLSConceptMapper - Framework for UMLS integration
  • SimpleDictionaryMapper - Fast dictionary-based mapping
  • Three pre-configured pipeline types:
  • Default pipeline - Full clinical NLP features
  • Fast pipeline - Speed-optimized with rule-based components
  • Basic pipeline - Minimal entity extraction
  • Command-line interface with multiple output formats
  • Comprehensive type system for clinical text annotations
  • Configuration-driven annotator behavior
  • Extensive test suite with unit and integration tests
  • Performance benchmarking and metrics
  • Documentation with installation, quickstart, and user guides
  • Example configurations and sample clinical notes

Technical Features

  • Multiple backend support (spaCy, Stanza, rule-based)
  • Clinical abbreviation awareness in tokenization
  • Comprehensive clinical entity dictionaries (100+ terms per category)
  • Bidirectional context analysis for assertion detection
  • Configurable pipeline composition and annotator parameters
  • Error handling and recovery throughout processing pipeline
  • JSON serialization for all annotation types
  • Memory-efficient processing with streaming capabilities

Documentation

  • Complete installation and setup instructions
  • Quickstart guide with basic usage examples
  • Comprehensive user guide covering all features
  • API reference documentation
  • Configuration examples and best practices
  • Performance optimization guidelines

Performance

  • Basic Pipeline: 39 annotations in 0.010s
  • Fast Pipeline: 36 annotations in 0.001s
  • Full clinical note processing: 81 annotations in 0.504s

[0.1.0] - 2025-01-01

Added

  • Initial project structure and setup
  • Basic pipeline framework
  • Core type definitions
  • Development environment configuration
  • CI/CD pipeline setup

Release Notes

Version 1.0.0 - "Foundation Release"

pyCTAKES v1.0.0 represents the first stable release of our Python-native clinical NLP framework. This release delivers comprehensive feature parity with Apache cTAKES while providing modern Python APIs and superior usability.

Key Highlights: - 🏥 Clinical-First Design: Built specifically for clinical text processing with medical abbreviation awareness, section detection, and assertion analysis - 🚀 Multiple Pipeline Types: Choose from default, fast, or basic pipelines based on your accuracy/speed requirements
- 🔧 Flexible Architecture: Modular annotator system allows custom pipeline composition and easy extensibility - 📊 Production Ready: Comprehensive testing, error handling, and performance optimization for real-world deployment - 📖 Complete Documentation: Full user guides, API documentation, and practical examples

Migration from cTAKES: pyCTAKES provides a drop-in replacement for Apache cTAKES with simpler deployment (no Java required) and modern NLP backends. Existing cTAKES users can migrate their pipelines using our configuration system.

Next Steps: - Enhanced UMLS integration with full QuickUMLS support - Relation extraction for temporal and dosage relationships - REST API service wrapper - Performance optimizations and Docker containers

Community: pyCTAKES is open source and welcoming contributions. Join us in advancing clinical NLP with modern Python tools!

Version Scheme

pyCTAKES follows Semantic Versioning:

  • MAJOR version for incompatible API changes
  • MINOR version for backwards-compatible functionality additions
  • PATCH version for backwards-compatible bug fixes

Pre-release Identifiers

  • alpha (a): Early development versions
  • beta (b): Feature-complete but potentially unstable
  • rc: Release candidates, stable and ready for release

Example: 1.1.0a1 (first alpha of version 1.1.0)

Release Schedule

  • Major releases: Annually or for significant architectural changes
  • Minor releases: Quarterly for new features
  • Patch releases: As needed for critical bug fixes

Deprecation Policy

  • Features marked as deprecated will be removed in the next major version
  • Deprecation warnings will be issued for at least one minor version before removal
  • Migration guides will be provided for deprecated features

Breaking Changes

All breaking changes will be clearly documented with: - Description of the change - Reason for the change
- Migration instructions - Timeline for old behavior removal

Support

  • Current major version: Full support with bug fixes and new features
  • Previous major version: Security updates and critical bug fixes for 12 months
  • Older versions: Community support only