Package: quickumls
NEW: v.1.4 supports starting multiple QuickUMLS matchers concurrently!I've finally added support forunqliteas an alternative to leveldb for storage of CUIs and Semantic Types (seeherefor more details). unqlite-backed QuickUMLS installation support multiple matchers running at the same time. Other than better multi-processing support, unqlite should have better support for unicode.QuickUMLSQuickUMLS (Soldaini and Goharian, 2016) is a tool for fast, unsupervised  biomedical concept extraction from medical text.
It takes advantage ofSimstring(Okazaki and Tsujii, 2010) for approximate string matching.
For more details on how QuickUMLS works, we remand to our paper.This project should be compatible with Python 3 (Python 2 isno longer supported) and run on any UNIX system (support for Windows is experimental, please report bugs!).If you find any bugs, please file an issue on GitHub or email the author atluca@ir.cs.georgetown.edu.InstallationObtain a UMLS installationThis tool requires you to have a valid UMLS installation on disk. To install UMLS, you must first obtain alicensefrom the National Library of Medicine; then you should download all UMLS files fromthis page; finally, you can install UMLS using theMetamorphoSystool asexplained in this guide.  The installation can be removed once the system has been initialized.Install QuickUMLS: You can do so by either runningpip install quickumlsorpython setup.py install. On macOS, using anaconda isstrongly recommended†.Create a QuickUMLS installationInitialize the system by runningpython -m quickumls.install <umls_installation_path> <destination_path>, where<umls_installation_path>is where the installation files are (in particular, we needMRCONSO.RRFandMRSTY.RRF) and<destination_path>is the directory where the QuickUmls data files should be installed. This process will take between 5 and 30 minutes depending how fast the CPU and the drive where UMLS and QuickUMLS files are stored are (on a system with a Intel i7 6700K CPU and a 7200 RPM hard drive, initialization takes 8.5 minutes).python -m quickumls.installsupports the following optional arguments:-L/--lowercase: if used, all concept terms are folded to lowercase before being processed. This option typically increases recall, but it might reduce precision;-U/--normalize-unicode: if used, expressions with non-ASCII characters are converted to the closest combination of ASCII characters.-E/--language: Specify the language to consider for UMLS concepts; by default, English is used. For a complete list of languages, please seethis table provided by NLM.-d/--database-backend: Specify which database backend to use for QuickUMLS. The two options areleveldbandunqlite. The latter supports multi-process reading and has better unicode compatibility, and it used as default for all new 1.4 installations; the former is still used as default when instantiating a QuickUMLS client. More info about differences between the two databases and migration info are availablehere.†: If the installation fails on macOS when using Anaconda, installleveldbfirst by runningconda install -c conda-forge python-leveldb.APIsA QuickUMLS object can be instantiated as follows:fromquickumlsimportQuickUMLSmatcher=QuickUMLS(quickumls_fp,overlapping_criteria,threshold,similarity_name,window,accepted_semtypes)Where:quickumls_fpis the directory where the QuickUMLS data files are installed.overlapping_criteria(optional, default: "score") is the criteria used to deal with overlapping concepts; choose "score" if the matching score of the concepts should be consider first, "length" if the longest should be considered first instead.threshold(optional, default: 0.7) is the minimum similarity value between strings.similarity_name(optional, default: "jaccard") is the name of similarity to use. Choose between "dice", "jaccard", "cosine", or "overlap".window(optional, default: 5) is the maximum number of tokens to consider for matching.accepted_semtypes(optional, default: seeconstants.py) is the set of UMLS semantic types concepts should belong to. Semantic types are identified by the letter "T" followed by three numbers (e.g., "T131", which identifies the type"Hazardous or Poisonous Substance"). Seeherefor the full list.To use the matcher, simply calltext="The ulna has dislocated posteriorly from the trochlea of the humerus."matcher.match(text,best_match=True,ignore_syntax=False)Setbest_matchtoFalseif you want to return overlapping candidates,ignore_syntaxtoTrueto disable all heuristics introduced in (Soldaini and Goharian, 2016).If the matcher throws a warning during initialization, readthis pageto learn why and how to stop it from doing so.spaCy pipeline componentQuickUMLS can be used for standalone processing but it can also be use as a component in a modular spaCy pipeline.  This follows traditional spaCy handling of concepts to be entity objects added to the Document object.  These entity objects contain the CUI, similarity score and Semantic Types in the spacy "underscore" object.Adding QuickUMLS as a component in a pipeline can be done as follows:fromquickumls.spacy_componentimportSpacyQuickUMLS# common English pipelinenlp=spacy.load('en_core_web_sm')quickumls_component=SpacyQuickUMLS(nlp,'PATH_TO_QUICKUMLS_DATA')nlp.add_pipe(quickumls_component)doc=nlp('Pt c/o shortness of breath, chest pain, nausea, vomiting, diarrrhea')forentindoc.ents:print('Entity text :{}'.format(ent.text))print('Label (UMLS CUI) :{}'.format(ent.label_))print('Similarity :{}'.format(ent._.similarity))print('Semtypes :{}'.format(ent._.semtypes))Server / Client SupportStarting with v.1.2, QuickUMLS includes a support for being used in a client-server configuration. That is, you can start one QuickUMLS server, and query it from multiple scripts using a client.To start the server, runpython -m quickumls.server:python-mquickumls.server/path/to/quickumls/files{-PQuickUMLSport}{-HQuickUMLShost}{QuickUMLSoptions}Host and port are optional; by default, QuickUMLS runs onlocalhost:4645. You can also pass any QuickUMLS option mentioned above to the server. To obtain a list of options for the server, runpython -m quickumls.server -h.To load the client, importget_quickumls_clientfromquickumls:fromquickumlsimportget_quickumls_clientmatcher=get_quickumls_client()text="The ulna has dislocated posteriorly from the trochlea of the humerus."matcher.match(text,best_match=True,ignore_syntax=False)The API of the client is the same of a QuickUMLS object.In case you wish to run the server in the background, you can do so as follows:nohuppython-mquickumls.server/path/to/QuickUMLS{serveroptions}>/dev/null2>&1&echo$!>nohup.pidWhen you are done, don't forget to stop the server by running.kill-9`catnohup.pid`rmnohup.pidReferencesOkazaki, Naoaki, and Jun'ichi Tsujii. "Simple and efficient algorithm for approximate dictionary matching." COLING 2010.Luca Soldaini and Nazli Goharian. "QuickUMLS: a fast, unsupervised approach for medical concept extraction." MedIR Workshop, SIGIR 2016.