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Medication Extraction from Clinical Data Using Frame Semantics

Medication Extraction from Clinical Data Using Frame Semantics. DIMITRIOS KOKKINAKIS Centre for Language Technology University of Gotehnburg dimitrios.kokkinakis@svenska.gu.se. OVERVIEW. Motivation Semantic Annotation of Corpora and Event-Based Information Extraction

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Medication Extraction from Clinical Data Using Frame Semantics

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  1. Medication Extraction from Clinical Data Using Frame Semantics DIMITRIOS KOKKINAKIS Centre for Language Technology University of Gotehnburg dimitrios.kokkinakis@svenska.gu.se

  2. OVERVIEW • Motivation • Semantic Annotation of Corpora and Event-Based Information Extraction • e.g. i2b2 Medication Challenge • Frame Semantics • Medical Frames • Pilot. Administration_of_Medication • Design and Resources (so far…) • Conclusion and Future Work

  3. MOTIVATION (EXTRACTION OF FACTS and EVENTS) Semantic annotation of corpora for mining complex relations and events has gained a considerable growing attention in the medical domain Goal (work in progress) to develop an appropriate infrastructure for automatic event labeling in the clinical domain using hybrid techniques (e.g. supervised machine learning, rules, lexicons, etc) Event extraction can be modeled as a sequential tagging problem, train and test data sets will be/are taken from Swedish medical corpora while the Swedish FrametNet++ provides the basis for the events’ description

  4. EVENT-BASED INFORMATION EXTRACTION Information extraction (IE) is a technology that has a direct correlation with frame-like structures in FrameNet; since templates in the context of IE are frame-like structures with slots representing event information. Most event-based IE approaches are designed to identify role fillers that appear as arguments to event verbs or nouns, either explicitly via syntactic relations or implicitly via proximity

  5. The ”Medication Challenge” i2b2… (2009) The Third i2b2 Workshop on NLP Challenges for Clinical Records (designed as an information extraction task) focused on the extraction of medications and medication-related information from discharge summaries

  6. The ”Medication Challenge” i2b2… (2009)

  7. ADVANTAGES OF STRUCTURED DATA… • get an overview of the medication ordered in diff dimensions • help organize and improve the presentation of EHR; advanced graphical presentation of EHR data • create the basis for data mining, evidence-based medicine; e.g. for the epidemiological analysis of adverse events • allow the automatic transmission of data to various registries • aggregate data from many patients in repositories, facilitating e.g. open comparisons • make the selection of more reliable quality comparisons between different parts of the country / world • create a database directly accessible to the research • allowing the generation of new hypotheses and new (semantic) relationships • improving patient safety, pharmacovigilance …

  8. FRAME SEMANTICS… The FrameNet approach is based on the linguistic theory of frame semantics supported by corpus evidence. A semantic frame is a script-like structure of concepts, which are linked to the meanings of linguistic units and associated with a specific event or state Each frame identifies a set of frame elements, which are frame specific semantic roles; both so called core roles, arguments, tightly coupled with the particular meaning of the frame and more generic non-core ones, adjuncts or modifiers which to large extent are event-independent semantic roles When using computers to extract semantic information for NLP tasks, FrameNet's semantic mapping provides a means for the computer to extract meaning from a string of words

  9. FRAME SEMANTICS… Thus, a word activates, or evokes, a frame of semantic knowledge relating to the specific concept it refers to. A semantic frame is a collection of facts that specify "characteristic features, attributes, and functions of a denotatum, and its characteristic interactions with things necessarily or typically associated with it". A semantic frame can also be defined as a coherent structure of related concepts that are related such that without knowledge of all of them, one does not have complete knowledge of any one E.g., one would not be able to understand the word sell without knowing anything about the situation of commercial transfer, which also involves a seller, a buyer, goods, money, the relation between the money and the goods and so on

  10. RELEVANT APPLICATIONS… FN began collaborations with two industrial partners this year. One is with a defense contractor to develop frames and annotation for reports written by U.S. soldiers after patrols in Afghanistan and Iraq. The other is a partnership with Siemens Research U.S. to develop frames and annotation for medical texts, such as medical textbooks and guidelines for the treatment of diseases. http://www.icsi.berkeley.edu/pubs/icsi/2011AnnualReport.pdf

  11. A slide from an LREC 2012 presentation (closing session)

  12. MEDICALLY ORIENTED FRAMES https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=frame_report&name=Medical_intervention

  13. Swedish MEDICALLY ORIENTED FRAMES Administration_of_medication Addiction Birth Death Experience_bodily_harm Falling_ill Health_response Institutionalization Medical_disorders Medical_instruments Medical_interaction_scenario Medical_professionals Medical_specialties Medical_treatment Observable_bodyparts People_by_disease Recovery … http://spraakbanken.gu.se/eng/research/swefn/development-version

  14. Example Frame: CURE http://spraakbanken.gu.se/eng/research/swefn/development-version

  15. Example http://spraakbanken.gu.se/eng/research/swefn/development-version

  16. Frame: Administration_of_Medication CORE Frame Elements NON-CORE Frame Elements

  17. Design so far… Resources in Use FASS is the Swedish national formulary: contains a list of medicines that are approved for prescription throughout Swedish SNOMED CT’s Substance hierarchy: contains “concepts that can be used for recording active chemical constituents of drug projects, food and chemical allergens, adverse reactions, toxicity or poisoning information, and physicians and nursing orders” <http://www.ihtsdo.org/snomed-ct/snomed-ct0/snomed-ct-hierarchies/substance/> Swedish MeSH’scategory D, Chemicals and Drugs (5,886) Drug lexicon extensions (e.g. generic expressions of drugs, detecting misspellings) List of relevant abbreviations+variants: iv, i.v., im, i.m. sc, s.c., po, p.o., vb, v.b., V b, T, inj., tbl, … …

  18. Design so far… Resources in Use • Named Entity Recognition for the relevant entities: • Drug Names • Time • Frequency • Terminology Recognition • MeSH • SNOMED CT • (ongoing) Manual annotation with the frame elements

  19. Richard Johansson, Karin Friberg Heppin, DimitriosKokkinakis. SemanticRoleLabeling with the Swedish FrameNet. Proceedings of the 8th International Conf on Language Resources and Evaluation (LREC'12), pp. 3697–3700. Istanbul, Turkey, 2012.

  20. CONCLUSIONS The driving force for the experiments is frame semantics, which allows us to work with a more holistic and detailed semantic event description than it is possible using for instance most traditional efforts based on binary relation extraction approaches Event extraction is more complicated and challenging than relation extraction since events usually have internal structure involving several entities as participants allowing a detailed representation of more complex statements Preliminary results suggest that SweFN++ seems a good start for annotating corpora. The role set described is general enough to capture a wide range of phenomena that characterize the majority of semantic arguments of general medical events

  21. FUTURE WORK • Need larger size of annotated corpora for larger scale • experiments (which are planned…) • We are currently working with: • extending/refining/encoding new frames according to the BFN descriptions • manually annotating larger corpora • investigate how existing frame descriptions can actually capture semantics • continue with more experiments (methods, software, larger data sets) for learning to annotate the arguments • using a richer set of features, and particularly syntactic • information and the distance between the arguments

  22. …related REFERENCES • Sigfried Gold, Noémie Elhadad, Xinxin Zhu, James J. Cimino, and George Hripcsak. Extracting Structured Medication Event Information from Discharge Summaries. AMIA Annu Symp Proc. 2008; 2008: 237–241. • Jon Patrick, Min Li. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. J Am Med Inf Assoc 2010;17:524e527. • Louise Deléger, Cyril Grouin, Pierre Zweigenbaum. Extracting medical information from narrative patient records: the case of medication-related information. J Am Med Inf Assoc 2010;17:555e558. • Son Doan, Lisa Bastarache, Sergio Klimkowski, Joshua C Denny, Hua Xu. Integrating existing natural language processing tools for medication extraction from discharge summaries. J Am Med Inf Assoc 2010;17:528e531. • Thierry Hamon, Natalia Grabar. Linguistic approach for identification of medication names and related information in clinical narratives. J Am Med Inf Assoc 2010;17:549e554. • Scott Russell Halgrim, Fei Xia, Imre Solti, Eithon Cadag, Özlem Uzuner. A cascade of classifiers for extracting medication information from discharge summaries. J of Biomed Sem 2011, 2(Suppl 3):S2

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