Electronic health record based algorithm to identify patients with autism spectrum disorder

PLoS ONE

Volumn 11, Issue 7, 2016, Pages

  Lingren, Todd ^a   Chen, Pei ^b   Bochenek, Joseph ^c   Doshi Velez, Finale ^d   Manning Courtney, Patty ^a,e   Bickel, Julie ^b   Welchons, Leah Wildenger ^b   Reinhold, Judy ^a,e   Bing, Nicole ^a,e   Ni, Yizhao ^a   Barbaresi, William ^b   Mentch, Frank ^f   Basford, Melissa ^g   Denny, Joshua ^c   Vazquez, Lyam ^f   Perry, Cassandra ^b   Namjou, Bahram ^a,h   Qiu, Haijun ^f   Connolly, John ^f   Abrams, Debra ^f   Holm, Ingrid A ^b,d   Cobb, Beth A ^a   Lingren, Nataline ^a   Solti, Imre ^a,h   Hakonarson, Hakon ^f,i   Kohane, Isaac S ^b,d   Harley, John ^a,e,j   Savova, Guergana ^b,d   more..

^a CINCINNATI CHILDREN'S HOSPITAL MEDICAL CENTER   (United States)

^b BOSTON CHILDREN S HOSPITAL   (United States)

^c VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e UNIVERSITY OF CINCINNATI   (United States)

^f CHILDREN S HOSPITAL OF PHILADELPHIA   (United States)

^g VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^h UNIVERSITY OF CINCINNATI COLLEGE OF MEDICINE   (United States)

ⁱ UNIVERSITY OF PENNSYLVANIA   (United States)

^j VETERANS AFFAIRS MEDICAL CENTER   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AUTISM SPECTRUM DISORDER; CHILD; COHORT ANALYSIS; ELECTRONIC HEALTH RECORD; FEMALE; HUMAN; MALE; PRESCHOOL CHILD;

ALGORITHMS; AUTISM SPECTRUM DISORDER; CHILD; CHILD, PRESCHOOL; COHORT STUDIES; ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; MALE;

EID: 84992530306     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0159621     Document Type: Article

References (45)

1. Prevalence of autism spectrum disorder among children aged 8 years–autism and developmental disabilities monitoring network, 11 sites, United Sates, 2010. MMWR. Surveillance Summary 63, 1–21 (2014).
2. Wing L, Gould J. Severe impairments of social interaction and associated abnormalities in children: epidemiology and classification. J Autism Dev Disord. 1979; 9(1):11–29. PMID: 155684
3. Ben-Sasson A, Cermak SA, Orsmond GI, Tager-Flusberg H, Kadlec MB, Carter AS. Sensory clusters of toddlers with autism spectrum disorders: differences in affective symptoms. J Child Psychol Psychiatry. 2008; 49(8):817–825 doi: 10.1111/j.1469-7610.2008.01899.x PMID: 18498344
4. Bitsika V, Sharpley CF, Orapeleng S. An exploratory analysis of the use of cognitive, adaptive and behavioural indices for cluster analysis of ASD subgroups. J Intellect Disabil Res. 2008; 52(11):973–985 doi: 10.1111/j.1365-2788.2008.01123.x PMID: 19017167
5. Hu VW, Steinberg ME. Novel clustering of items from the Autism Diagnostic Interview-Revised to define phenotypes within autism spectrum disorders. Autism Res. 2009; 2(2):67–77 doi: 10.1002/aur.72 PMID: 19455643
6. Wiggins LD, Robins DL, Adamson LB, Bakeman R, Henrich CC. Support for a dimensional view of autism spectrum disorders in toddlers. J Autism Dev Disord. 2012; 42(2): 191–200. doi: 10.1007/ s10803-011-1230-0 PMID: 21448751
7. Doshi-Velez F, Ge Y, Kohane I. (2014) Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics 133, e54–e63. doi: 10.1542/peds.2013-0819 PMID: 24323995
8. http://emerge.mc.vanderbilt.edu/
9. Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. J Am Med Inform Assoc. 2013 Dec; 20(e2):e206–211. PMID: 24302669 doi: 10.1136/amiajnl-2013-002428
10. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Denny JC. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc. 2013 Mar 26; PMID: 23531748
11. Peissig PL, Rasmussen LV, Berg RL, Linneman JG, McCarty CA, Starren JB. Importance of multimodal approaches to effectively identify cataract cases from electronic health records. J Am Med Inform Assoc. 2012 Mar 1; 19(2):225–234. PMID: 22319176 doi: 10.1136/amiajnl-2011-000456
12. Kho AN, Hayes MG, Rasmussen-Torvik L, Pacheco JA, Thompson WK, Lowe WL. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inform Assoc. 2012 Mar 1; 19(2):212–218. PMID: 22101970 doi: 10.1136/ amiajnl-2011-000439
13. Denny JC, Crawford DC, Ritchie MD, Bielinski SJ, Basford MA, de Andrade M. Variants Near FOXE1 Are Associated with Hypothyroidism and Other Thyroid Conditions: Using Electronic Medical Records for Genome- and Phenome-wide Studies. Am J Hum Genet. 2011 Oct 7; 89(4):529–542. PMID: 21981779 doi: 10.1016/j.ajhg.2011.09.008
14. Schildcrout JS, Basford MA, Pulley JM, Masys DR, Roden DM, Denny JC. An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records. J Biomed Inform. 2010 Dec; 43(6):914–923. PMID: 20688191 doi: 10.1016/j.jbi.2010.07.011
15. Kullo IJ, Fan J, Pathak J, Savova GK, Ali Z, Chute CG. Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. J Am Med Inform Assoc. 2010 Sep-Oct; 17(5):568–74. doi: 10.1136/jamia.2010.004366 PMID: 20819866
16. Lin C, Karlson EW, Canhao H, Miller TA, Dligach D, Chen PJ, et al. Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records. PLOS One. August 16, 2013. doi: 10. 1371/journal.pone.0069932
17. Ananthakrishnan AN, Cai T, Savova G, Cheng SC, Chen P, Perez RG, et al. Improving Case Definition of Crohn's Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing: A Novel Informatics Approach. Inflammatory bowel diseases. 2013 Jun; 19(7):1411–20. doi: 10.1097/MIB.0b013e31828133fd PMID: 23567779
18. Arthritis Care Res (Hoboken). 2010 Aug; 62(8):1120–7. doi: 10.1002/acr.20184
19. Liao KP, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, Raychaudhuri S, et al. Electronic medical records for discovery research in rheumatoid arthritis.
20. Kohane IS. Using electronic health records to drive discovery in disease genomics. Nature Reviews Genetics 12, 417–428 (June 2011) doi: 10.1038/nrg2999 PMID: 21587298
21. Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N, et al. (2011). Electronic medical records for genetic research: results of the eMERGE consortium. Science translational medicine, 3 (79), 79re1–79re1. doi: 10.1126/scitranslmed.3001807 PMID: 21508311
22. Unified Medical Language System (UMLS). Available: http://www.nlm.nih.gov/research/umls/
23. Savova G, Masanz J, Ogren P, Zheng J, Soh S, Karin Kipper-Schuler, et al. 2010. Mayo Clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. JAMIA 2010; 17:507–513 doi: 10.1136/jamia.2009.001560 PMID: 20819853
24. http://ctakes.apache.org
25. Barbaresi WJ, Katusic SK, Colligan RC, Weaver AL, Jacobsen SJ. "The incidence of autism in Olmsted County, Minnesota, 1976–1997: results from a population-based study." Archives of pediatrics & adolescent medicine 159.1 (2005): 37–44.
26. NLM. UMLS Knowledge Sources, 2013AA Edition. Available: https://uts.nlm.nih.gov/
27. Chen H, Fuller SS, Friedman C, Hersh W. Medical informatics: knowledge management and data mining in biomedicine. Springer. 2010.
28. Sarkar IN. Methods in biomedical informatics: a pragmatic approach. Elsevier Science & Technology Books. 2013.
29. Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V. (2000). Feature selection for SVMs. In NIPS (Vol. 12, pp. 668–674).
30. Chen YW, Lin CJ. Combining SVMs with various feature selection strategies. Feature Extraction. 2006; 207:315–24.
31. Nguyen MH, De la Torre F. (2010). Optimal feature selection for support vector machines. Pattern recognition, 43(3), 584–591.
32. Guyon I, Weston J, Barnhill S, Vapnik V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1–3), 389–422.
33. Cross-Disorder Group of the Psychiatric Genomics Consortium, C.-D. G. & others (2013) Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371 doi: 10.1016/S0140-6736(12)62129-1 PMID: 23453885
34. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, et al. (2013). Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. Journal of the American Medical Informatics Association, 20(e1), e147–e154. doi: 10.1136/ amiajnl-2012-000896 PMID: 23531748
35. Boland MR, Hripcsak G, Shen Y, Chung WK, Weng C. (2013). Defining a comprehensive verotype using electronic health records for personalized medicine. Journal of the American Medical Informatics Association, 20(e2), e232–e238. doi: 10.1136/amiajnl-2013-001932 PMID: 24001516
36. Denny JC. (2012). Mining electronic health records in the genomics era. PLoS computational biology, 8(12), e1002823. doi: 10.1371/journal.pcbi.1002823 PMID: 23300414
37. Kohane IS, McMurry A, Weber G, MacFadden D, Rappaport L, Kunkel L, et al. (2012) The co-morbidity burden of children and young adults with autism spectrum disorders. PloS one 7, e33224. doi: 10. 1371/journal.pone.0033224 PMID: 22511918
38. Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, et al. (2013). Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature biotechnology.
39. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. (2011) Scikit-learn: Machine Learning in {P}ython. Journal of Machine Learning Research 12, 2825–2830.
40. Ester M, Kriegel HP, Sander J, Xu X. A Density Based Algorithm for Discovering Clusters in Large Spatial Databases, in the Proceedings of 2nd International Conf. on Data Mining, 1996.
41. Manning CD, Raghavan P, Schutze H. Introduction to information retrieval. Cambridge University Press. 2008.
42. Van der Maaten L, Hinton G. (2008) Visualizing data using t-SNE. Journal of Machine Learning Research 9, 85.
43. Held FP, Blyth F, Gnjidic D, Hirani V, Naganathan V, Waite LM, et al. Association rules analysis of comorbidity and multimorbidity: The Concord Health and Aging in Men Project. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2015:glv181.
44. Ortiz A, Morer A, Moreno E, Plana M, Cordovilla C, Lázaro L. Clinical significance of psychiatric comorbidity in children and adolescents with obsessive–compulsive disorder: subtyping a complex disorder. European archives of psychiatry and clinical neuroscience. 2016; 266(3):199–208. doi: 10.1007/ s00406-015-0642-9 PMID: 26374751
45. Nierenberg AA, Sylvia LG, Leon AC, Reilly-Harrington NA, Shesler LW, McElroy SL, et al. Clinical and Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder (Bipolar CHOICE): a pragmatic trial of complex treatment for a complex disorder. Clinical Trials. 2013:1740774513512184.