Automatic Mapping Clinical Notes to Medical Terminologies

Automatic mapping of key concepts from clinical notes to a terminology is an important task to achieve for extraction of the clinical information locked in clinical notes and patient reports. The present paper describes a system that automatically maps free text into a medical reference terminology. The algorithm utilises Natural Language Processing (NLP) techniques to enhance a lexical token matcher. In addition, this algorithm is able to identify negative concepts as well as performing term qualification. The algorithm has been implemented as a web based service running at a hospital to process real-time data and demonstrated that it worked within acceptable time limits and accuracy limits for them. However broader acceptability of the algorithm will require comprehensive evaluations.

[1]  Adwait Ratnaparkhi,et al.  A Maximum Entropy Approach to Identifying Sentence Boundaries , 1997, ANLP.

[2]  Carol Friedman,et al.  Research Paper: A General Natural-language Text Processor for Clinical Radiology , 1994, J. Am. Medical Informatics Assoc..

[3]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.

[4]  Cynthia Brandt,et al.  Research Paper: UMLS Concept Indexing for Production Databases: A Feasibility Study , 2001, J. Am. Medical Informatics Assoc..

[5]  Alan R. Aronson,et al.  Towards linking patients and clinical information: detecting UMLS concepts in e-mail , 2003, J. Biomed. Informatics.

[6]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[7]  Kent A. Spackman,et al.  SNOMED clinical terms: overview of the development process and project status , 2001, AMIA.

[8]  Peter L. Elkin,et al.  UMLS Concept Indexing for Production Databases: A Feasibility Study , 2001, J. Am. Medical Informatics Assoc..

[9]  Prakash M. Nadkarni,et al.  Research Paper: Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS , 2001, J. Am. Medical Informatics Assoc..

[10]  Philip J. B. Brown,et al.  Evaluation of the quality of information retrieval of clinical findings from a computerized patient database using a semantic terminological model. , 2000, Journal of the American Medical Informatics Association : JAMIA.

[11]  J. C. Klimczak SNOMED international, the systematized nomenclature of human and veterinary medicine , 1994 .

[12]  Christopher G. Chute,et al.  A clinically derived terminology: qualification to reduction , 1997, AMIA.

[13]  Dean F Sittig,et al.  Application of Information Technology j MediClass : A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical , 2005 .

[14]  Sophia Ananiadou,et al.  Developing a Robust Part-of-Speech Tagger for Biomedical Text , 2005, Panhellenic Conference on Informatics.

[15]  Peter L. Elkin,et al.  A controlled trial of automated classification of negation from clinical notes , 2005, BMC Medical Informatics Decis. Mak..

[16]  Dan Klein,et al.  Improved Identification of Noun Phrases in Clinical Radiology Reports Using a High-Performance Statistical Natural Language Parser Augmented with the UMLS Specialist Lexicon , 2005 .

[17]  Hongfang Liu,et al.  Research Paper: Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS , 2002, J. Am. Medical Informatics Assoc..

[18]  D. Lindberg,et al.  The Unified Medical Language System , 1993, Methods of Information in Medicine.

[19]  Craig A. Morioka,et al.  IndexFinder: A Method of Extracting Key Concepts from Clinical Texts for Indexing , 2003, AMIA.

[20]  William R. Hersh,et al.  Information Retrieval in Medicine: The SAPHIRE Experience , 1995, J. Am. Soc. Inf. Sci..