Summary of Product Characteristics content extraction for a safe drugs usage

The use of medications has a central role in health care provision, yet on occasion, it may injure the person taking them as result of adverse drug events. A correct drug choice must be modulated to acknowledge both patients' status and drug-specific information. However, this information is locked in free-text and, as such, cannot be actively accessed and elaborated by computerized applications. The goal of this work lies in extracting content (active ingredient, interaction effects, etc.) from the Summary of Product Characteristics, focusing mainly on drug-related interactions, following a machine learning based approach. We compare two state of the art classifiers: conditional random fields with support vector machines. To this end, we introduce a corpus of 100 interaction sections, hand annotated with 13 labels that have been derived from a previously developed conceptual model. The results of our empirical analysis demonstrate that the two models perform well. They exhibit similar overall performance, with an overall accuracy of about 91%.

[1]  Andrew J. Rohm,et al.  Just what the doctor ordered: The role of information sensitivity and trust in reducing medical information privacy concern , 2004 .

[2]  Ambulatory Computerized Provider Order Entry ( CPOE ) : Findings from the AHRQ Health IT Portfolio , 2008 .

[3]  P. Aspden,et al.  Preventing Medication Errors , 2007 .

[4]  Özlem Uzuner,et al.  Extracting medication information from clinical text , 2010, J. Am. Medical Informatics Assoc..

[5]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[6]  Domonkos Tikk,et al.  Improving textual medication extraction using combined conditional random fields and rule-based systems , 2010, J. Am. Medical Informatics Assoc..

[7]  G. Hripcsak,et al.  Extracting Findings from Narrative Reports: Software Transferability and Sources of Physician Disagreement , 1998, Methods of Information in Medicine.

[8]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[9]  S D Small,et al.  Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. , 1995, JAMA.

[10]  P. Haug,et al.  Computerized extraction of coded findings from free-text radiologic reports. Work in progress. , 1990, Radiology.

[11]  David L. Reich,et al.  Extraction and Mapping of Drug Names from Free Text to a Standardized Nomenclature , 2007, AMIA.

[12]  Rainu Kaushal,et al.  Center for Information Technology Leadership , 2003 .

[13]  R. Kane,et al.  Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors. , 2007, Health services research.

[14]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[15]  Antoine Bordes,et al.  Sequence Labelling SVMs Trained in One Pass , 2008, ECML/PKDD.

[16]  D. Bates,et al.  Relationship between medication errors and adverse drug events , 1995, Journal of General Internal Medicine.

[17]  Stéfan Jacques Darmoni,et al.  Automatic Construction of Dictionaries, Application to Product Characteristics Indexing , 2009, MIE.

[18]  ELSKE AMMENWERTH,et al.  Review Paper: The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review , 2008, J. Am. Medical Informatics Assoc..

[19]  Peter Spyns.  Natural Language Processing in Medicine: An Overview , 1996, Methods of Information in Medicine.

[20]  D. Bates,et al.  Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. , 2003, Archives of internal medicine.

[21]  D A Evans,et al.  Automating concept identification in the electronic medical record: an experiment in extracting dosage information. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[22]  Wei Wu,et al.  The Effect of Computerized Physician Order Entry with Clinical Decision Support on the Rates of Adverse Drug Events: A Systematic Review , 2008, Journal of General Internal Medicine.

[23]  Marco Baroni,et al.  Morph-it! A free corpus-based morphological resource for the Italian language , 2005 .

[24]  Shuying Shen,et al.  Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents , 2010, J. Am. Medical Informatics Assoc..

[25]  T. Clemmer,et al.  A computer-assisted management program for antibiotics and other antiinfective agents. , 1998, The New England journal of medicine.

[26]  Allen C. Browne,et al.  The Role of Lexical Knowledge in Biomedical Text Understanding. , 1987 .

[27]  G Hripcsak,et al.  Natural language processing and its future in medicine. , 1999, Academic medicine : journal of the Association of American Medical Colleges.

[28]  Son Doan,et al.  Application of information technology: MedEx: a medication information extraction system for clinical narratives , 2010, J. Am. Medical Informatics Assoc..

[29]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[30]  George Hripcsak,et al.  Extracting Structured Medication Event Information from Discharge Summaries , 2008, AMIA.

[31]  Anoop D Shah,et al.  An algorithm to derive a numerical daily dose from unstructured text dosage instructions , 2006, Pharmacoepidemiology and drug safety.

[32]  J Starren,et al.  Architectural requirements for a multipurpose natural language processor in the clinical environment. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[33]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[34]  N. Laird,et al.  Incidence of Adverse Drug Events and Potential Adverse Drug Events: Implications for Prevention , 1995 .

[35]  Son Doan,et al.  Integrating existing natural language processing tools for medication extraction from discharge summaries , 2010, J. Am. Medical Informatics Assoc..

[36]  Sophia Ananiadou,et al.  Text Mining for Biology And Biomedicine , 2005 .

[37]  Peggy L. Peissig,et al.  Study of Effect of Drug Lexicons on Medication Extraction from Electronic Medical Records , 2004, Pacific Symposium on Biocomputing.

[38]  Hanna M. Wallach,et al.  Conditional Random Fields: An Introduction , 2004 .

[39]  César de Pablo-Sánchez,et al.  Using a shallow linguistic kernel for drug-drug interaction extraction , 2011, J. Biomed. Informatics.

[40]  L. Ohno-Machado.  Journal of Biomedical Informatics , 2001 .