Building a Natural Language Processing Tool to Identify Patients with High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes

Academic Emergency Medicine

Volumn 23, Issue 5, 2016, Pages 628-636

  Doan, Son ^a   Maehara, Cleo K ^a   Chaparro, Juan D ^a   Lu, Sisi ^b   Liu, Ruiling ^c   Graham, Amanda ^d   Berry, Erika ^e   Hsu, Chun Nan ^a   Kanegaye, John T ^e,f   Lloyd, David D ^d,g   Ohno Machado, Lucila ^a   Burns, Jane C ^e,f   Tremoulet, Adriana H ^e,f  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of Pittsburgh   (United States)

^c UNIVERSITY OF TEXAS   (United States)

^d CHILDREN'S HEALTHCARE OF ATLANTA   (United States)

^e UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^f RADY CHILDREN'S HOSPITAL   (United States)

^g EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHILD; CLINICAL CLASSIFICATION; CLINICAL FEATURE; COHORT ANALYSIS; CONFERENCE PAPER; EMERGENCY WARD; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MUCOCUTANEOUS LYMPH NODE SYNDROME; NATURAL LANGUAGE PROCESSING; PERFORMANCE; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; UNIFIED MEDICAL LANGUAGE SYSTEM; DATA MINING; ELECTRONIC HEALTH RECORD; HOSPITAL EMERGENCY SERVICE; PROCEDURES;

CHILD; DATA MINING; ELECTRONIC HEALTH RECORDS; EMERGENCY SERVICE, HOSPITAL; HUMANS; MUCOCUTANEOUS LYMPH NODE SYNDROME; NATURAL LANGUAGE PROCESSING; SENSITIVITY AND SPECIFICITY;

EID: 84963815781     PISSN: 10696563     EISSN: 15532712     Source Type: Journal    
DOI: 10.1111/acem.12925     Document Type: Conference Paper

References (35)

1. Newburger JW, Takahashi M, Gerber MA, et al., Diagnosis, treatment, and long-term management of Kawasaki disease: a statement for health professionals from the Committee on Rheumatic Fever, Endocarditis and Kawasaki Disease, Council on Cardiovascular Disease in the Young, American Heart Association. Circulation 2004; 110: 2747-71.
2. Newburger JW, Takahashi M, Burns JC, et al., The treatment of Kawasaki syndrome with intravenous gamma globulin. N Engl J Med 1986; 315: 341-7.
3. Newburger JW, Takahashi M, Beiser AS, et al., A single intravenous infusion of gamma globulin as compared with four infusions in the treatment of acute Kawasaki syndrome. N Engl J Med 1991; 324: 1633-9.
4. Wilder MS, Palinkas LA, Kao AS, Bastian JF, Turner CL, Burns JC,. Delayed diagnosis by physicians contributes to the development of coronary artery aneurysms in children with Kawasaki syndrome. Pediatr Infect Dis J 2007; 26: 256-60.
5. Anderson MS, Todd JK, Glodé MP,. Delayed diagnosis of Kawasaki syndrome: an analysis of the problem. Pediatrics 2005; 115: e428-33.
6. Ling XB, Lau K, Kanegaye JT, et al., A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses. BMC Med 2011; 9: 130.
7. Ling XB, Kanegaye JT, Ji J, et al., Point-of-care differentiation of Kawasaki disease from other febrile illnesses. J Pediatr 2013; 162: 183-8.e3.
8. Gausche-Hill M, Schmitz C, Lewis RJ,. Pediatric preparedness of US emergency departments: a 2003 survey. Pediatrics 2007; 120: 1229-37.
9. Kanegaye JT, Van Cott E, Tremoulet AH, et al., Lymph-node-first presentation of Kawasaki disease compared with bacterial cervical adenitis and typical Kawasaki disease. J Pediatr 2013; 162: 1259-63, 1263.e1-2.
10. Minich LL, Sleeper LA, Atz AM, et al., Delayed diagnosis of Kawasaki disease: what are the risk factors? Pediatrics 2007; 120: e1434-40.
11. Senzaki H,. Long-term outcome of Kawasaki disease. Circulation 2008; 118: 2763-72.
12. Gordon JB, Kahn AM, Burns JC,. When children with Kawasaki disease grow up: myocardial and vascular complications in adulthood. J Am Coll Cardiol 2009; 54: 1911-20.
13. Daniels LB, Tjajadi MS, Walford HH, et al., Prevalence of Kawasaki disease in young adults with suspected myocardial ischemia. Circulation 2012; 125: 2447-53.
14. Rizk SR, El Said G, Daniels LB, et al., Acute myocardial ischemia in adults secondary to missed Kawasaki disease in childhood. Am J Cardiol 2015; 115: 423-7.
15. Ohno-Machado L, Bafna V, Boxwala AA, et al., iDASH: integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc 19: 196-201.
16. Stenetorp P, Pyysalo S, Topic G, Ohta T, Ananiadou S, Tsujii J,. BRAT: a web-based tool for NLP-assisted text annotation. Proc Demonstr 13th Conf Eur Chapter Assoc Comput Linguist 2012; 102-7.
17. Lindberg DA, Humphreys BL, McCray AT,. The Unified Medical Language System. Methods Inf Med 1993; 32: 281-91.
18. Aronson AR,. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of the AMIA Symposium. American Medical Informatics Association; 2001. p. 17-21.
19. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG,. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001; 34: 301-10.
20. Manning CD, Schütze H,. Foundations of Statistical Natural Language Processing. Cambridge (MA): The MIT Press, 1999.
21. Chapman WW, Dowling JN, Wagner MM,. Fever detection from free-text clinical records for biosurveillance. J Biomed Inform 2004; 37: 120-7.
22. Uzuner Ö, South BR, Shen S, DuVall SL,. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011; 18: 552-6.
23. Uzuner Ö, Solti I, Cadag E,. Extracting medication information from clinical text. J Am Med Inform Assoc 2010; 17: 514-8.
24. Pathak J, Wang J, Kashyap S, et al., Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience. J Am Med Inform Assoc 2011; 18: 376-86.
25. Kho AN, Pacheco JA, Peissig PL, et al., Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med 2011; 3: 79re1.
26. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF,. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008; 128-44.
27. Kimia AA, Capraro AJ, Hummel D, Johnston P, Harper MB,. Utility of lumbar puncture for first simple febrile seizure among children 6 to 18 months of age. Pediatrics 2009; 123: 6-12.
28. Kimia A, Ben-Joseph EP, Rudloe T, et al., Yield of lumbar puncture among children who present with their first complex febrile seizure. Pediatrics 2010; 126: 62-9.
29. Savova G, Deleger L, Solti I, Pestian J, Dexheimer J,. Natural Language Processing-Applications in pediatric research. In: Pediatric Biomedical Informatics: Computer Applications in Pediatric Research (Translational Bioinformatics). New York: Springer Verlag, 2013. p. 173-92.
30. Deleger L, Brodzinski H, Zhai H, et al., Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department. J Am Med Inform Assoc 2013; 20: e212-20.
31. Connolly B, Matykiewicz P, Cohen K, et al., Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals. J Am Med Inform Assoc 2014; 21: 866-70.
32. Wu ST, Juhn YJ, Sohn S, Liu H,. Patient-level temporal aggregation for text-based asthma status ascertainment. J Am Med Inform Assoc 2014; 21: 876-84.
33. Doan S, Conway M, Phuong TM, Ohno-Machado L,. Natural language processing in biomedicine: a unified system architecture overview. Methods Mol Biol 2014; 1168: 275-94.
34. Jain NL, Knirsch CA, Friedman C, Hripcsak G,. Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports. In: Proc AMIA Annu Fall Symp. American Medical Informatics Association, 1996. p. 542-6.
35. Murff HJ, FitzHenry F, Matheny ME, et al., Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011; 306: 848-55.