Jak mogę użyć biblioteki MIST do deidentyfikacji tekstu?

Question

Zastanawiam się, jak używać Czy MIST library de-identyfikacji tekstu, na przykład, przekształcając

Patient ID: P89474 

Mary Phillips is a 45-year-old woman with a history of diabetes. 
She arrived at New Hope Medical Center on August 5 complaining 
of abdominal pain. Dr. Gertrude Philippoussis diagnosed her 
with appendicitis and admitted her at 10 PM. 

do

Patient ID: [ID] 

[NAME] is a [AGE]-year-old woman with a history of diabetes. 
She arrived at [HOSPITAL] on [DATE] complaining 
of abdominal pain. Dr. [PHYSICIAN] diagnosed her 
with appendicitis and admitted her at 10 PM. 

Mam wędrował poprzez dokumentację ale nie szczęścia tak daleko.

Answer 1

Ta odpowiedź została przetestowana na systemie Windows 7 SP1 x64 Ultimate z Anaconda Python 2.7.11 x64 i MIST 2.0.4. MIST 2.0.4 nie działa z Pythonem 3.x (zgodnie z instrukcją, sam tego nie testowałem).

MIST (MITER Identification Scrubber Toolkit) [1] to personalizacja MAT (MITER Adnotation Toolkit), która jest narzędziem do automatycznego oznaczania dokumentów lub z ludźmi (w tym ostatnim dostarcza GUI przez serwer WWW). Automatyczny tagger jest oparty na Carafe (ConditionAl RAndom Fields) [2], który jest implementacją OCaml losowych zmiennych losowych (CRF).

MIST nie jest wyposażony w żaden wyszkolony model i ma tylko ~ 10 krótkich, niemedycznych dokumentów opatrzonych typową klasą NER (np. Organizacja i osoba).

De-id (de-identification) to proces oznaczania PHI (Private Health Information) w dokumencie i zastępowania ich fałszywymi danymi. Zignorujmy teraz zamianę PHI i skupmy się na tagowaniu. W celu oznaczenia dokumentu (np. Notatki pacjenta), MAT stosuje się do typowego schematu uczenia maszynowego: CRF musi zostać przeszkolony na etykietowanym zbiorze danych (= zestaw etykietowanych dokumentów), a następnie używamy go do oznaczania dokumentów bez etykiety.

Podstawową koncepcją techniczną w MAT są zadania. Zadanie jest zbiorem czynności zwanych przepływami pracy, które można podzielić na kroki. Rozpoznanie nazwy obiektu (NER) to jedno zadanie. De-id to kolejne zadanie (głównie NER nastawiony na teksty medyczne): innymi słowy, MIST jest tylko jednym zadaniem MAT (w rzeczywistości 3: core, HIPAA i AMIA, Core jest zadaniem nadrzędnym, podczas gdy HIPAA i AMIA są dwoma różne znaczniki). Kroki są na przykład tokenizacją, tagowaniem lub czyszczeniem. Przepływy pracy to tylko lista kroków, które można wykonać.

Mając to na uwadze, tutaj jest kod Microsoft Windows:

####### 
rem Instructions for Windows 7 SP1 x64 Ultimate 
rem Installing MIST: set MAT_PKG_HOME depending on where you downloaded it 
SET MAT_PKG_HOME=C:\Users\Francky\Downloads\MIST_2_0_4\MIST_2_0_4\src\MAT 
SET TMP=C:\Users\Francky\Downloads\MIST_2_0_4\MIST_2_0_4\temp 
cd C:\Users\Francky\Downloads\MIST_2_0_4\MIST_2_0_4 
python install.py 

# MAT is now installed. We'll show how to use it for NER. 
# We will be taking snippets from some of the 8 tutorials. 
# A lot of the tutorial content are about the annotation GUI, 
# which we don't care here. 

# Tuto 1: install task 
cd %MAT_PKG_HOME% 
bin\MATManagePluginDirs.cmd install %CD%\sample\ne 

# Tuto 2: build model (i.e., train it on labeled dataset) 
bin\MATModelBuilder.cmd --task "Named Entity" --model_file %TMP%\ne_model^
--input_files "%CD%\sample\ne\resources\data\json\*.json" 

# Tuto 2: Add trained model as the default model 
bin\MATModelBuilder.cmd --task "Named Entity" --save_as_default_model^
--input_files "%CD%\sample\ne\resources\data\json\*.json" 

# Tudo 5: use CLI -> prepare the document 
bin\MATEngine.cmd --task "Named Entity" --workflow Demo --steps "zone,tokenize"^
--input_file %CD%\sample\ne\resources\data\raw\voa2.txt --input_file_type raw^
--output_file %CD%\voa2_txt.json --output_file_type mat-json 

# Tuto 5: use CLI -> tag the document 
bin\MATEngine.cmd --task "Named Entity" --workflow Demo --steps "tag"^
--input_file %CD%\voa2_txt.json --input_file_type mat-json^
--output_file %CD%\voa2_txt.json --output_file_type mat-json^
--tagger_local 

NER jest teraz zrobić.

Oto te same instrukcje Ubuntu 14.04.4 LTS x64:

####### 
# Instructions for Ubuntu 14.04.4 LTS x64 
# Installing MIST: set MAT_PKG_HOME depending on where you downloaded it 
export MAT_PKG_HOME=/home/ubuntu/mist/MIST_2_0_4/MIST_2_0_4/src/MAT 
export TMP=/home/ubuntu/mist/MIST_2_0_4/MIST_2_0_4/temp 
mkdir $TMP 
cd /home/ubuntu/mist/MIST_2_0_4/MIST_2_0_4/ 
python install.py 

# MAT is now installed. We'll show how to use it for NER. 
# We will be taking snippets from some of the 8 tutorials. 
# A lot of the tutorial content are about the annotation GUI, 
# which we don't care here. 

# Tuto 1: install task 
cd $MAT_PKG_HOME 
bin/MATManagePluginDirs install $PWD/sample/ne 

# Tuto 2: build model (i.e., train it on labeled dataset) 
bin/MATModelBuilder --task "Named Entity" --model_file $TMP/ne_model \ 
--input_files "$PWD/sample/ne/resources/data/json/*.json" 

# Tuto 2: Add trained model as the default model 
bin/MATModelBuilder --task "Named Entity" --save_as_default_model \ 
--input_files "$PWD/sample/ne/resources/data/json/*.json" 

# Tudo 5: use CLI -> prepare the document 
bin/MATEngine --task "Named Entity" --workflow Demo --steps "zone,tokenize" \ 
--input_file $PWD/sample/ne/resources/data/raw/voa2.txt --input_file_type raw \ 
--output_file $PWD/voa2_txt.json --output_file_type mat-json 

# Tuto 5: use CLI -> tag the document 
bin/MATEngine --task "Named Entity" --workflow Demo --steps "tag" \ 
--input_file $PWD/voa2_txt.json --input_file_type mat-json \ 
--output_file $PWD/voa2_txt.json --output_file_type mat-json \ 
--tagger_local 

---

Aby uruchomić de-id, nie ma potrzeby instalowania zadania de-id są wstępnie -instalowany. Istnieją 2 zadania de-id (\MIST_2_0_4\src\tasks\HIPAA\task.xml i \MIST_2_0_4\src\tasks\AMIA\task.xml). Nie są one wyposażone w żaden wyszkolony model ani etykietowany zestaw danych, więc możesz chcieć uzyskać pewne dane pod numerem Physician notes with annotated PHI.

Dla Microsoft Windows (testowane z Windows 7 SP1 x64 Ostatecznego):

Aby trenować model (można zastąpić HIPAA Deidentification z AMIA Deidentification zależności od tagset chcesz używać):

bin\MATModelBuilder.cmd --task "HIPAA Deidentification"^
--save_as_default_model --nthreads=3 --max_iterations=15^
--lexicon_dir="%CD%\sample\mist\gazetteers"^
--input_files "%CD%\sample\mist\i2b2-60-00-40\train\*.json" 

Aby uruchomić wyszkolony model w jednym pliku:

bin\MATEngine --task "HIPAA Deidentification" --workflow Demo^
--input_file .\note.txt --input_file_type raw^
--output_file .\note.json --output_file_type mat-json^
--tagger_local^
--steps "clean,zone,tag" 

Aby uruchomić przeszkolony Model w jednym katalogu:

bin\MATEngine --task "HIPAA Deidentification" --workflow Demo^
--input_dir "%CD%\sample\test" --input_file_type raw^
--output_dir "%CD%\sample\test" --output_file_type mat-json^
--tagger_local^
--steps "clean,zone,tag" 

Jak zwykle, można określić format pliku wejściowego być JSON:

bin\MATEngine --task "HIPAA Deidentification" --workflow Demo^
--input_dir "%CD%\sample\mist\i2b2-60-00-40\test" --input_file_type mat-json^
--output_dir "%CD%\sample\mist\i2b2-60-00-40\test_out" --output_file_type mat-json^
--tagger_local --steps "tag" 

Dla Ubuntu 14.04.4 LTS x64:

trenować model (można zamienić HIPAA Deidentification na AMIA Deidentification w zależności od zestawu tagów, którego chcesz użyć):

bin/MATModelBuilder --task "HIPAA Deidentification" \ 
--save_as_default_model --nthreads=20 --max_iterations=15 \ 
--lexicon_dir="$PWD/sample/mist/gazetteers" \ 
--input_files "$PWD/sample/mist/i2b2-60-00-40/train/*.json" 

Aby uruchomić wyszkolony modelu na jednym pliku:

bin/MATEngine --task "HIPAA Deidentification" --workflow Demo \ 
--input_file ./note.txt --input_file_type raw \ 
--output_file ./note.json --output_file_type mat-json \ 
--tagger_local \ 
--steps "clean,zone,tag" 

Aby uruchomić wyszkolony modelu w jednym katalogu:

bin/MATEngine --task "HIPAA Deidentification" --workflow Demo \ 
--input_dir "$PWD/sample/test" --input_file_type raw \ 
--output_dir "$PWD/sample/test" --output_file_type mat-json \ 
--tagger_local \ 
--steps "clean,zone,tag" 

Jak zwykle, można określić format pliku wejściowego być JSON:

bin/MATEngine --task "HIPAA Deidentification" --workflow Demo \ 
--input_dir "$PWD/sample/mist/i2b2-60-00-40/test" --input_file_type mat-json \ 
--output_dir "$PWD/sample/mist/i2b2-60-00-40/test_out" --output_file_type mat-json \ 
--tagger_local --steps "tag" 

---

Typowe komunikaty o błędach:

• raise PluginError, "Carafe not configured properly for this task and workflow: " + str(e) (podczas próby oznaczenia dokumentu): często oznacza to, że nie określono modelu. Musisz zdefiniować model domyślny lub użyć --tagger_model /path/to/model/.

• Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded (podczas treningu modelu): jest to łatwe, aby przejść ponad limit heap_size (domyślnie jest to 2GB). Możesz zwiększyć wartość heap_size za pomocą parametru --heap_size. Przykład (Linux):

  bin/MATModelBuilder --task "HIPAA Deidentification" \ 
  --save_as_default_model --nthreads=20 --max_iterations=15 \ 
  --lexicon_dir="$PWD/sample/mist/gazetteers" \ 
  --heap_size=60G \ 
  --input_files "$PWD/sample/mist/mimic-140-20-40/train/*.json" 
  

---

[1] Jan Aberdeen, Samuel Bayer, Reyyan Yeniterzi Ben Wellner Cheryl Clark David Hanauer Bradley Malin, Lynette Hirschman, mitrę identyfikacja płuczka Toolkit : projektowanie, szkolenie i ocena, Int. J. Med. Informatyka 79 (12) (2010) 849-859, http://dx.doi.org/10.1016/j.ijmedinf.2010.09.007.

[2] B. Wellner, Modele sekwencji i metody rankingowe dla Parsowanie [Ph.D. Rozprawa]. Brandeis University, Waltham, MA, 2009.http://www.cs.brandeis.edu/~wellner/pubs/wellner_dissertation.pdf

---

Dokumentacja MATModelBuilder.cmd:

Usage: MATModelBuilder.cmd [task option] [config name option] [other options] 

Options: 
    -h, --help   show this help message and exit 

    Task option: 
    --task=task   name of the task to use. Must be the first argument, 
         if present. Obligatory if the system knows of more 
         than one task. Known tasks are: AMIA Deidentification, 
         Named Entity, HIPAA Deidentification, Enhanced Named 
         Entity 

    Config name option: 
    --config_name=name name of the model build config to use. Must be the 
         first argument after --task, if present. Optional. 
         Default model build config will be used if no config 
         is specified. 

    Control options: 
    --version   Print version number and exit 
    --debug    Enable debug output. 
    --subprocess_debug=int 
         Set the subprocess debug level to the value provided, 
         overriding the global setting. 0 disables, 1 shows 
         some subprocess activity, 2 shows all subprocess 
         activity. 
    --subprocess_statistics 
         Enable subprocess statistics (memory/time), if the 
         capability is available and it isn't globally enabled. 
    --tmpdir_root=dir Override the default system location for temporary 
         files. If the directory doesn't exist, it will be 
         created. Use this feature to control where temporary 
         files are created, for added security, or in 
         conjunction with --preserve_tempfiles, as a debugging 
         aid. 
    --preserve_tempfiles 
         Preserve the temporary files created, as a debugging 
         aid. 
    --verbose_config If specified, print to stderr the source of each MAT 
         configuration variable the first time it's accessed. 

    Options for model class creation: 
    --partial_training_on_gold_only 
         When the trainer is presented with partially tagged 
         documents, by default MAT will ask it to train on all 
         annotated segments, completed or not. If this flag is 
         specified, only completed segments should be used for 
         training. 
    --feature_spec=FEATURE_SPEC 
         path to the Carafe feature spec file to use. Optional 
         if feature_spec is set in the <build_settings> for the 
         relevant model config in the task.xml file for the 
         task. 
    --training_method=TRAINING_METHOD 
         If present, specify a training method other than the 
         standard method. Currently, the only recognized value 
         is psa. The psa method is noticeably faster, but may 
         result in somewhat poorer results. You can use a value 
         of '' to override a previously specified training 
         method (e.g., a default method in your task). 
    --max_iterations=MAX_ITERATIONS 
         number of iterations for the optimized PSA training 
         mechanism to use. A value between 6 and 10 is 
         appropriate. Overrides any possible default in 
         <build_settings> for the relevant model config in the 
         task.xml file for the task. 
    --lexicon_dir=LEXICON_DIR 
         If present, the name of a directory which contains a 
         Carafe training lexicon. This pathname should be an 
         absolute pathname, and should have a trailing slash. 
         The content of the directory should be a set of files, 
         each of which contains a sequence of tokens, one per 
         line. The name of the file will be used as a training 
         feature for the token. Overrides any possible default 
         in <build_settings> for the relevant model config in 
         the task.xml file for the task. 
    --parallel   If present, parallelizes the feature expectation 
         computation, which reduces the clock time of model 
         building when multiple CPUs are available 
    --nthreads=NTHREADS 
         If --parallel is used, controls the number of threads 
         used for training. 
    --gaussian_prior=GAUSSIAN_PRIOR 
         A positive float, default is 10.0. See the jCarafe 
         docs for details. 
    --no_begin   Don't introduce begin states during training. Useful 
         if you're certain that you won't have any adjacent 
         spans with the same label. See the jCarafe 
         documentation for more details. 
    --l1    Use L1 regularization for PSA training. See the 
         jCarafe docs for details. 
    --l1_c=L1_C   Change the penalty factor for the L1 regularizer. See 
         the jCarafe docs for details. 
    --heap_size=HEAP_SIZE 
         If present, specifies the -Xmx argument for the Java 
         JVM 
    --stack_size=STACK_SIZE 
         If present, specifies the -Xss argument for the Java 
         JVM 
    --tags=TAGS   if present, a comma-separated list of tags to pass to 
         the training engine instead of the full tag set for 
         the task (used to create per-tag pre-tagging models 
         for multi-stage training and tagging) 
    --pre_models=PRE_MODELS 
         if present, a comma-separated list of glob-style 
         patterns specifying the models to include as pre- 
         taggers. 
    --add_tokens_internally 
         If present, Carafe will use its internal tokenizer to 
         tokenize the document before training. If your 
         workflow doesn't tokenize the document, you must 
         provide this flag, or Carafe will have no tokens to 
         base its training on. We recommend strongly that you 
         tokenize your documents separately; you should not use 
         this flag. 
    --word_properties=WORD_PROPERTIES 
         See the jCarafe docs for --word-properties. 
    --word_scores=WORD_SCORES 
         See the jCarafe docs for --word-scores. 
    --learning_rate=LEARNING_RATE 
         See the jCarafe docs for --learning-rate. 
    --disk_cache=DISK_CACHE 
         See the jCarafe docs for --disk_cache. 

    Input options: 
    --input_dir=dir  A directory, all of whose files will be used in the 
         model construction. Can be repeated. May be specified 
         with --input_files. 
    --input_files=re A glob-style pattern describing full pathnames to use 
         in the model construction. May be specified with 
         --input_dir. Can be repeated. 
    --file_type=fake-xml-inline | mat-json | xml-inline 
         The file type of the input. One of fake-xml-inline, 
         mat-json, xml-inline. Default is mat-json. 
    --encoding=encoding 
         The encoding of the input. The default is the 
         appropriate default for the file type. 

    Output options: 
    --model_file=file Location to save the created model. The directory must 
         already exist. Obligatory if --save_as_default_model 
         isn't specified. 
    --save_as_default_model 
         If the the task.xml file for the task specifies the 
         <default_model> element, save the model in the 
         specified location, possibly overriding any existing 
         model. 

Dokumentacja MATEngine:

Usage: MATEngine [core options] [input/output/task options] [other options] 

Options: 
    -h, --help   show this help message and exit 

    Core options: 
    --other_app_dir=dir 
         additional directory to load a task from. Optional and 
         repeatable. 
    --settings_file=file 
         a file of settings to use which overwrites existing 
         settings. The file should be a Python config file in 
         the style of the template in 
         etc/MAT_settings.config.in. Optional. 
    --task=task   name of the task to use. Obligatory if the system 
         knows of more than one task. Known tasks are: AMIA 
         Deidentification, Named Entity, HIPAA 
         Deidentification, Enhanced Named Entity 
    --version   Print version number and exit 
    --debug    Enable debug output. 
    --subprocess_debug=int 
         Set the subprocess debug level to the value provided, 
         overriding the global setting. 0 disables, 1 shows 
         some subprocess activity, 2 shows all subprocess 
         activity. 
    --subprocess_statistics 
         Enable subprocess statistics (memory/time), if the 
         capability is available and it isn't globally enabled. 
    --tmpdir_root=dir Override the default system location for temporary 
         files. If the directory doesn't exist, it will be 
         created. Use this feature to control where temporary 
         files are created, for added security, or in 
         conjunction with --preserve_tempfiles, as a debugging 
         aid. 
    --preserve_tempfiles 
         Preserve the temporary files created, as a debugging 
         aid. 
    --verbose_config If specified, print to stderr the source of each MAT 
         configuration variable the first time it's accessed.