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Changelog

v0.10.2

Changed

  • eds.span_qualifier qualifiers argument now automatically adds the underscore prefix if not present

Fixed

  • Fix imports of components declared in spacy_factories entry points
  • Support pendulum v3
  • AsList errors are now correctly reported
  • eds.span_qualifier saved configuration during to_disk is now longer null

v0.10.1

Changed

  • Small regex matching performance improvement, up to 1.25x faster (e.g. eds.measurements)

Fixed

  • Microgram scale is now correctly 1/1000g and inverse meter now 1/100 inverse cm.
  • We now isolate some of edsnlp components (trainable pipes that require ml dependencies) in a new edsnlp_factories entry points to prevent spacy from auto-importing them.
  • TNM scores followed by a space are now correctly detected
  • Removed various short TNM false positives (e.g., "PT" or "a T") and false negatives
  • The Span value extension is not more forcibly overwritten, and user assigned values are returned by Span._.value in priority, before the aggregated span._.get(span.label_) getter result (#220)
  • Enable mmap during multiprocessing model transfers
  • RegexMatcher now supports all alignment modes (strict, expand, contract) and better handles partial doc matching (#201).
  • on_ent_only=False/True is now supported again in qualifier pipes (e.g., "eds.negation", "eds.hypothesis", ...)

v0.10.0

Added

  • New add unified edsnlp.data api (json, brat, spark, pandas) and LazyCollection object to efficiently read / write data from / to different formats & sources.
  • New unified processing API to select the execution execution backends via data.set_processing(...)
  • The training scripts can now use data from multiple concatenated adapters
  • Support quantized transformers (compatible with multiprocessing as well !)

Changed

  • edsnlp.pipelines has been renamed to edsnlp.pipes, but the old name is still available for backward compatibility
  • Pipes (in edsnlp/pipes) are now lazily loaded, which should improve the loading time of the library.
  • to_disk methods can now return a config to override the initial config of the pipeline (e.g., to load a transformer directly from the path storing its fine-tuned weights)
  • The eds.tokenizer tokenizer has been added to entry points, making it accessible from the outside
  • Deprecate old connectors (e.g. BratDataConnector) in favor of the new edsnlp.data API
  • Deprecate old pipe wrapper in favor of the new processing API

Fixed

  • Support for pydantic v2
  • Support for python 3.11 (not ci-tested yet)

v0.10.0beta1

Large refacto of EDS-NLP to allow training models and performing inference using PyTorch as the deep-learning backend. Rather than a mere wrapper of Pytorch using spaCy, this is a new framework to build hybrid multi-task models.

To achieve this, instead of patching spaCy's pipeline, a new pipeline was implemented in a similar fashion to aphp/edspdf#12. The new pipeline tries to preserve the existing API, especially for non-machine learning uses such as rule-based components. This means that users can continue to use the library in the same way as before, while also having the option to train models using PyTorch. We still use spaCy data structures such as Doc and Span to represent the texts and their annotations.

Otherwise, changes should be transparent for users that still want to use spacy pipelines with nlp = spacy.blank('eds'). To benefit from the new features, users should use nlp = edsnlp.blank('eds') instead.

Added

  • New pipeline system available via edsnlp.blank('eds') (instead of spacy.blank('eds'))
  • Use the confit package to instantiate components
  • Training script with Pytorch only (tests/training/) and tutorial
  • New trainable embeddings: eds.transformer, eds.text_cnn, eds.span_pooler embedding contextualizer pipes
  • Re-implemented the trainable NER component and trainable Span qualifier with the new system under eds.ner_crf and eds.span_classifier
  • New efficient implementation for eds.transformer (to be used in place of spacy-transformer)

Changed

  • Pipe registering: Language.factory -> edsnlp.registry.factory.register via confit
  • Lazy loading components from their entry point (had to patch spacy.Language.init) to avoid having to wrap every import torch statement for pure rule-based use cases. Hence, torch is not a required dependency

v0.9.2

Changed

  • Fix matchers to skip pipes with assigned extensions that are not required by the matcher during the initialization

v0.9.1

Changed

  • Improve negation patterns
  • Abstent disorders now set the negation to True when matched as ABSENT
  • Default qualifier is now None instead of False (empty string)

Fixed

  • span_getter is not incompatible with on_ents_only anymore
  • ContextualMatcher now supports empty matches (e.g. lookahead/lookbehind) in assign patterns

v0.9.0

Added

  • New to_duration method to convert an absolute date into a date relative to the note_datetime (or None)

Changes

  • Input and output of components are now specified by span_getter and span_setter arguments.
  • 💥 Score / disorders / behaviors entities now have a fixed label (passed as an argument), instead of being dynamically set from the component name. The following scores may have a different name than the current one in your pipelines:
  • eds.emergency.gemsa → emergency_gemsa
  • eds.emergency.ccmu → emergency_ccmu
  • eds.emergency.priority → emergency_priority
  • eds.charlson → charlson
  • eds.elston_ellis → elston_ellis
  • eds.SOFA → sofa
  • eds.adicap → adicap
  • eds.measuremets → size, weight, ... instead of eds.size, eds.weight, ...
  • eds.dates now separate dates from durations. Each entity has its own label:
  • spans["dates"] → entities labelled as date with a span._.date parsed object
  • spans["durations"] → entities labelled as duration with a span._.duration parsed object
  • the "relative" / "absolute" / "duration" mode of the time entity is now stored in the mode attribute of the span._.date/duration
  • the "from" / "until" period bound, if any, is now stored in the span._.date.bound attribute
  • to_datetime now only return absolute dates, converts relative dates into absolute if doc._.note_datetime is given, and None otherwise

Fixed

  • export_to_brat issue with spans of entities on multiple lines.

v0.8.1 (2023-05-31)

Fix release to allow installation from source

v0.8.0 (2023-05-24)

Added

Changed

  • Disable EDSMatcher preprocessing auto progress tracking by default
  • Moved dependencies to a single pyproject.toml: support for pip install -e '.[dev,docs,setup]'
  • ADICAP matcher now allow dot separators (e.g. B.H.HP.A7A0)

Fixed

  • Abbreviation and number tokenization issues in the eds tokenizer
  • eds.adicap : reparsed the dictionnary used to decode the ADICAP codes (some of them were wrongly decoded)
  • Fix build for python 3.9 on Mac M1/M2 machines.

v0.7.4 (2022-12-12)

Added

  • eds.history : Add the option to consider only the closest dates in the sentence (dates inside the boundaries and if there is not, it takes the closest date in the entire sentence).
  • eds.negation : It takes into account following past participates and preceding infinitives.
  • eds.hypothesis: It takes into account following past participates hypothesis verbs.
  • eds.negation & eds.hypothesis : Introduce new patterns and remove unnecessary patterns.
  • eds.dates : Add a pattern for preceding relative dates (ex: l'embolie qui est survenue à 10 jours).
  • Improve patterns in the eds.pollution component to account for multiline footers
  • Add QuickExample object to quickly try a pipeline.
  • Add UMLS terminology matcher eds.umls
  • New RegexMatcher method to create spans from groupdicts
  • New eds.dates option to disable time detection

Changed

  • Improve date detection by removing false positives

Fixed

  • eds.hypothesis : Remove too generic patterns.
  • EDSTokenizer : It now tokenizes "rechereche d'" as ["recherche", "d'"], instead of ["recherche", "d", "'"].
  • Fix small typos in the documentation and in the docstring.
  • Harmonize processing utils (distributed custom_pipe) to have the same API for Pandas and Pyspark
  • Fix BratConnector file loading issues with complex file hierarchies

v0.7.2 (2022-10-26)

Added

  • Improve the eds.history component by taking into account the date extracted from eds.dates component.
  • New pop up when you click on the copy icon in the termynal widget (docs).
  • Add NER eds.elston-ellis pipeline to identify Elston Ellis scores
  • Add flags=re.MULTILINE to eds.pollution and change pattern of footer

Fixed

  • Remove the warning in the eds.sections when eds.normalizer is in the pipe.
  • Fix filter_spans for strictly nested entities
  • Fill eds.remove-lowercase "assign" metadata to run the pipeline during EDSPhraseMatcher preprocessing
  • Allow back spaCy components whose name contains a dot (forbidden since spaCy v3.4.2) for backward compatibility.

v0.7.1 (2022-10-13)

Added

  • Add new patterns (footer, web entities, biology tables, coding sections) to pipeline normalisation (pollution)

Changed

  • Improved TNM detection algorithm
  • Account for more modifiers in ADICAP codes detection

Fixed

  • Add nephew, niece and daughter to family qualifier patterns
  • EDSTokenizer (spacy.blank('eds')) now recognizes non-breaking whitespaces as spaces and does not split float numbers
  • eds.dates pipeline now allows new lines as space separators in dates

v0.7.0 (2022-09-06)

Added

  • New nested NER trainable nested_ner pipeline component
  • Support for nested entities and attributes in BratDataConnector
  • Pytorch wrappers and experimental training utils
  • Add attribute section to entities
  • Add new cases for separator pattern when components of the TNM score are separated by a forward slash
  • Add NER eds.adicap pipeline to identify ADICAP codes
  • Add patterns to pollution pipeline and simplifies activating or deactivating specific patterns

Changed

  • Simplified the configuration scheme of the pollution pipeline
  • Update of the ContextualMatcher (and all pipelines depending on it), rendering it more flexible to use
  • Rename R component of score TNM as "resection_completeness"

Fixed

  • Prevent section titles from capturing surrounding tokens, causing overlaps (#113)
  • Enhance existing patterns for section detection and add patterns for previously ignored sections (introduction, evolution, modalites de sortie, vaccination) .
  • Fix explain mode, which was always triggered, in eds.history factory.
  • Fix test in eds.sections. Previously, no check was done
  • Remove SOFA scores spurious span suffixes

v0.6.2 (2022-08-02)

Added

  • New SimstringMatcher matcher to perform fuzzy term matching, and algorithm parameter in terminology components and eds.matcher component
  • Makefile to install,test the application and see the documentation

Changed

  • Add consultation date pattern "CS", and False Positive patterns for dates (namely phone numbers and pagination).
  • Update the pipeline score eds.TNM. Now it is possible to return a dictionary where the results are either str or int values

Fixed

  • Add new patterns to the negation qualifier
  • Numpy header issues with binary distributed packages
  • Simstring dependency on Windows

v0.6.1 (2022-07-11)

Added

  • Now possible to provide regex flags when using the RegexMatcher
  • New ContextualMatcher pipe, aiming at replacing the AdvancedRegex pipe.
  • New as_ents parameter for eds.dates, to save detected dates as entities

Changed

  • Faster eds.sentences pipeline component with Cython
  • Bump version of Pydantic in requirements.txt to 1.8.2 to handle an incompatibility with the ContextualMatcher
  • Optimise space requirements by using .csv.gz compression for verbs

Fixed

  • eds.sentences behaviour with dot-delimited dates (eg 02.07.2022, which counted as three sentences)

v0.6.0 (2022-06-17)

Added

  • Complete revamp of the measurements detection pipeline, with better parsing and more exhaustive matching
  • Add new functionality to the method Span._.date.to_datetime() to return a result infered from context for those cases with missing information.
  • Force a batch size of 2000 when distributing a pipeline with Spark
  • New patterns to pipeline eds.dates to identify cases where only the month is mentioned
  • New eds.terminology component for generic terminology matching, using the kb_id_ attribute to store fine-grained entity label
  • New eds.cim10 terminology matching pipeline
  • New eds.drugs terminology pipeline that maps brand names and active ingredients to a unique ATC code

v0.5.3 (2022-05-04)

Added

  • Support for strings in the example utility
  • TNM detection and normalisation with the eds.TNM pipeline
  • Support for arbitrary callback for Pandas multiprocessing, with the callback argument

v0.5.2 (2022-04-29)

Added

  • Support for chained attributes in the processing pipelines
  • Colour utility with the category20 colour palette

Fixed

  • Correct a REGEX on the date detector (both nov and nov. are now detected, as all other months)

v0.5.1 (2022-04-11)

Fixed

  • Updated Numpy requirements to be compatible with the EDSPhraseMatcher

v0.5.0 (2022-04-08)

Added

  • New eds language to better fit French clinical documents and improve speed
  • Testing for markdown codeblocks to make sure the documentation is actually executable

Changed

  • Complete revamp of the date detection pipeline, with better parsing and more exhaustive matching
  • Reimplementation of the EDSPhraseMatcher in Cython, leading to a x15 speed increase

v0.4.4

  • Add measures pipeline
  • Cap Jinja2 version to fix mkdocs
  • Adding the possibility to add context in the processing module
  • Improve the speed of char replacement pipelines (accents and quotes)
  • Improve the speed of the regex matcher

v0.4.3

  • Fix regex matching on spans.
  • Add fast_parse in date pipeline.
  • Add relative_date information parsing

v0.4.2

  • Fix issue with dateparser library (see scrapinghub/dateparser#1045)
  • Fix attr issue in the advanced-regex pipelin
  • Add documentation for eds.covid
  • Update the demo with an explanation for the regex

v0.4.1

  • Added support to Koalas DataFrames in the edsnlp.processing pipe.
  • Added eds.covid NER pipeline for detecting COVID19 mentions.

v0.4.0

  • Profound re-write of the normalisation :
  • The custom attribute CUSTOM_NORM is completely abandoned in favour of a more spacyfic alternative
  • The normalizer pipeline modifies the NORM attribute in place
  • Other pipelines can modify the Token._.excluded custom attribute
  • EDS regex and term matchers can ignore excluded tokens during matching, effectively adding a second dimension to normalisation (choice of the attribute and possibility to skip pollution tokens regardless of the attribute)
  • Matching can be performed on custom attributes more easily
  • Qualifiers are regrouped together within the edsnlp.qualifiers submodule, the inheritance from the GenericMatcher is dropped.
  • edsnlp.utils.filter.filter_spans now accepts a label_to_remove parameter. If set, only corresponding spans are removed, along with overlapping spans. Primary use-case: removing pseudo cues for qualifiers.
  • Generalise the naming convention for extensions, which keep the same name as the pipeline that created them (eg Span._.negation for the eds.negation pipeline). The previous convention is kept for now, but calling it issues a warning.
  • The dates pipeline underwent some light formatting to increase robustness and fix a few issues
  • A new consultation_dates pipeline was added, which looks for dates preceded by expressions specific to consultation dates
  • In rule-based processing, the terms.py submodule is replaced by patterns.py to reflect the possible presence of regular expressions
  • Refactoring of the architecture :
  • pipelines are now regrouped by type (core, ner, misc, qualifiers)
  • matchers submodule contains RegexMatcher and PhraseMatcher classes, which interact with the normalisation
  • multiprocessing submodule contains spark and local multiprocessing tools
  • connectors contains Brat, OMOP and LabelTool connectors
  • utils contains various utilities
  • Add entry points to make pipeline usable directly, removing the need to import edsnlp.components.
  • Add a eds namespace for components: for instance, negation becomes eds.negation. Using the former pipeline name still works, but issues a deprecation warning.
  • Add 3 score pipelines related to emergency
  • Add a helper function to use a spaCy pipeline as a Spark UDF.
  • Fix alignment issues in RegexMatcher
  • Change the alignment procedure, dropping clumsy numpy dependency in favour of bisect
  • Change the name of eds.antecedents to eds.history. Calling eds.antecedents still works, but issues a deprecation warning and support will be removed in a future version.
  • Add a eds.covid component, that identifies mentions of COVID
  • Change the demo, to include NER components

v0.3.2

  • Major revamp of the normalisation.
  • The normalizer pipeline now adds atomic components (lowercase, accents, quotes, pollution & endlines) to the processing pipeline, and compiles the results into a new Doc._.normalized extension. The latter is itself a spaCy Doc object, wherein tokens are normalised and pollution tokens are removed altogether. Components that match on the CUSTOM_NORM attribute process the normalized document, and matches are brought back to the original document using a token-wise mapping.
  • Update the RegexMatcher to use the CUSTOM_NORM attribute
  • Add an EDSPhraseMatcher, wrapping spaCy's PhraseMatcher to enable matching on CUSTOM_NORM.
  • Update the matcher and advanced pipelines to enable matching on the CUSTOM_NORM attribute.
  • Add an OMOP connector, to help go back and forth between OMOP-formatted pandas dataframes and spaCy documents.
  • Add a reason pipeline, that extracts the reason for visit.
  • Add an endlines pipeline, that classifies newline characters between spaces and actual ends of line.
  • Add possibility to annotate within entities for qualifiers (negation, hypothesis, etc), ie if the cue is within the entity. Disabled by default.

v0.3.1

  • Update dates to remove miscellaneous bugs.
  • Add isort pre-commit hook.
  • Improve performance for negation, hypothesis, antecedents, family and rspeech by using spaCy's filter_spans and our consume_spans methods.
  • Add proposition segmentation to hypothesis and family, enhancing results.

v0.3.0

  • Renamed generic to matcher. This is a non-breaking change for the average user, adding the pipeline is still :
nlp.add_pipe("matcher", config=dict(terms=dict(maladie="maladie")))
  • Removed quickumls pipeline. It was untested, unmaintained. Will be added back in a future release.
  • Add score pipeline, and charlson.
  • Add advanced-regex pipeline
  • Corrected bugs in the negation pipeline

v0.2.0

  • Add negation pipeline
  • Add family pipeline
  • Add hypothesis pipeline
  • Add antecedents pipeline
  • Add rspeech pipeline
  • Refactor the library :
  • Remove the rules folder
  • Add a pipelines folder, containing one subdirectory per component
  • Every component subdirectory contains a module defining the component, and a module defining a factory, plus any other utilities (eg terms.py)

v0.1.0

First working version. Available pipelines :

  • section
  • sentences
  • normalization
  • pollution