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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: other |
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multilinguality: monolingual |
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bigbio_license_shortname: DUA |
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pretty_name: n2c2 2009 Medications |
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homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ |
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bigbio_pubmed: True |
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bigbio_public: False |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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--- |
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# Dataset Card for n2c2 2009 Medications |
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## Dataset Description |
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|
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- **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ |
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- **Pubmed:** True |
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- **Public:** False |
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- **Tasks:** NER |
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The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records |
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focused on the identification of medications, their dosages, modes (routes) of administration, |
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frequencies, durations, and reasons for administration in discharge summaries. |
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The third i2b2 challenge—that is, the medication challenge—extends information |
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extraction to relation extraction; it requires extraction of medications and |
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medication-related information followed by determination of which medication |
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belongs to which medication-related details. |
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The medication challenge was designed as an information extraction task. |
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The goal, for each discharge summary, was to extract the following information |
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on medications experienced by the patient: |
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1. Medications (m): including names, brand names, generics, and collective names of prescription substances, |
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over the counter medications, and other biological substances for which the patient is the experiencer. |
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2. Dosages (do): indicating the amount of a medication used in each administration. |
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3. Modes (mo): indicating the route for administering the medication. |
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4. Frequencies (f): indicating how often each dose of the medication should be taken. |
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5. Durations (du): indicating how long the medication is to be administered. |
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6. Reasons (r): stating the medical reason for which the medication is given. |
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7. Certainty (c): stating whether the event occurs. Certainty can be expressed by uncertainty words, |
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e.g., “suggested”, or via modals, e.g., “should” indicates suggestion. |
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8. Event (e): stating on whether the medication is started, stopped, or continued. |
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9. Temporal (t): stating whether the medication was administered in the past, |
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is being administered currently, or will be administered in the future, to the extent |
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that this information is expressed in the tense of the verbs and auxiliary verbs used to express events. |
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10. List/narrative (ln): indicating whether the medication information appears in a |
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list structure or in narrative running text in the discharge summary. |
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The medication challenge asked that systems extract the text corresponding to each of the fields |
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for each of the mentions of the medications that were experienced by the patients. |
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The values for the set of fields related to a medication mention, if presented within a |
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two-line window of the mention, were linked in order to create what we defined as an ‘entry’. |
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If the value of a field for a mention were not specified within a two-line window, |
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then the value ‘nm’ for ‘not mentioned’ was entered and the offsets were left unspecified. |
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Since the dataset annotations were crowd-sourced, it contains various violations that are handled |
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throughout the data loader via means of exception catching or conditional statements. e.g. |
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annotation: anticoagulation, while in text all words are to be separated by space which |
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means words at end of sentence will always contain `.` and hence won't be an exact match |
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i.e. `anticoagulation` != `anticoagulation.` from doc_id: 818404 |
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## Citation Information |
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|
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``` |
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@article{DBLP:journals/jamia/UzunerSC10, |
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author = { |
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Ozlem Uzuner and |
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Imre Solti and |
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Eithon Cadag |
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}, |
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title = {Extracting medication information from clinical text}, |
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journal = {J. Am. Medical Informatics Assoc.}, |
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volume = {17}, |
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number = {5}, |
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pages = {514--518}, |
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year = {2010}, |
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url = {https://doi.org/10.1136/jamia.2010.003947}, |
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doi = {10.1136/jamia.2010.003947}, |
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timestamp = {Mon, 11 May 2020 22:59:55 +0200}, |
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biburl = {https://dblp.org/rec/journals/jamia/UzunerSC10.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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