Assessing Pneumonia Identification from Time-Ordered Narrative Reports

In this paper, we present a natural language processing system that can be used in hospital surveillance applications with the purpose of identifying patients with pneumonia. For this purpose, we built a sequence of supervised classifiers, where the dataset corresponding to each classifier consists of a restricted set of time-ordered narrative reports. In this way the pneumonia surveillance application will be able to invoke the most suitable classifier for each patient based on the period of time that has elapsed since the patient was admitted into the hospital. Our system achieves significantly better results when compared with a baseline previously proposed for pneumonia identification.

[1]  Wenqian Shang,et al.  A novel feature selection algorithm for text categorization , 2007, Expert Syst. Appl..

[2]  G. P. Gravlee.  Incidence and Patterns of Adverse Event Onset During the First 60 Days After Ventricular Assist Device Implantation , 2010 .

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

[4]  C. Goss,et al.  Genetic variation in the FAS gene and associations with acute lung injury. , 2011, American journal of respiratory and critical care medicine.

[5]  Richard Sinnott,et al.  A federated data collection application for the prediction of adverse hypotensive events , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[6]  Dunja Mladenic,et al.  Feature selection on hierarchy of web documents , 2003, Decis. Support Syst..

[7]  Shuying Shen,et al.  2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text , 2011, J. Am. Medical Informatics Assoc..

[8]  Fei Xia,et al.  Identifying Patients with Pneumonia from Free-Text Intensive Care Unit Reports , 2011 .

[9]  Carol Friedman,et al.  Extracting Information on Pneumonia in Infants Using Natural Language Processing of Radiology Reports , 2003, BioNLP@ACL.

[10]  Peter J. Haug,et al.  Automatic identification of pneumonia related concepts on chest x-ray reports , 1999, AMIA.

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

[12]  M. N. Lutfiyya,et al.  Diagnosis and treatment of community-acquired pneumonia. , 2006, American family physician.

[13]  S. Trent Rosenbloom,et al.  NLP-based Identification of Pneumonia Cases from Free-Text Radiological Reports , 2008, AMIA.

[14]  János Csirik,et al.  The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.

[15]  Cosmin Adrian Bejan,et al.  Pneumonia identification using statistical feature selection , 2012, J. Am. Medical Informatics Assoc..

[16]  Peter J. Haug,et al.  Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports , 2000, J. Am. Medical Informatics Assoc..

[17]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[18]  Mark E Glickman,et al.  Modeling the Effects of Genetic Factors on Late-Onset Diseases in Cohort Studies , 2002, Lifetime data analysis.

[19]  Peng Huang,et al.  A latent model approach to define event onset time in the presence of measurement error. , 2009, Statistics and its interface.

[20]  Wendy W. Chapman,et al.  ConText: An Algorithm for Identifying Contextual Features from Clinical Text , 2007, BioNLP@ACL.

[21]  Peter J. Haug,et al.  Combining decision support methodologies to diagnose pneumonia , 2001, AMIA.

[22]  Peter J. Haug,et al.  Comparing expert systems for identifying chest x-ray reports that support pneumonia , 1999, AMIA.

[23]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[24]  Peter J. Haug,et al.  A Comparison of Classification Algorithms to Automatically Identify Chest X-Ray Reports That Support Pneumonia , 2001, J. Biomed. Informatics.

[25]  J. Bartlett,et al.  Infectious Diseases Society of America/American Thoracic Society Consensus Guidelines on the Management of Community-Acquired Pneumonia in Adults , 2007, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[26]  Eneida A. Mendonça,et al.  Use of computerized surveillance to detect nosocomial pneumonia in neonatal intensive care unit patients. , 2004, American journal of infection control.