Mining the electronic health record for disease knowledge

Methods in Molecular Biology

Volumn 1159, Issue , 2014, Pages 269-286

  Chen, Elizabeth S ^a   Sarkar, Indra Neil ^a  

^a NONE

Author keywords

Data mining; Data privacy and security; Data warehouse; Electronic health record; Knowledge discovery in databases; Natural language processing; Standards; Text mining

Indexed keywords

ARTICLE; CONCEPTUAL FRAMEWORK; DATA MINING; DATA PROCESSING; ELECTRONIC MEDICAL RECORD; KNOWLEDGE BASE; NATURAL LANGUAGE PROCESSING; PRIORITY JOURNAL; PRIVACY; STANDARD; VALIDATION STUDY; HUMAN; PROCEDURES;

DATA MINING; ELECTRONIC HEALTH RECORDS; HUMANS; KNOWLEDGE BASES; NATURAL LANGUAGE PROCESSING;

EID: 84927515532     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-0709-0_15     Document Type: Article

References (123)

1. Institute of Medicine (U.S.), Committee on Improving the Patient Record (eds), Dick RS, Steen EB, Detmer DE (1997) The computerbased patient record: an essential technology for health care. Revised edition. National Academy Press, Washington, DC
2. Stewart WF, Shah NR, Selna MJ, Paulus RA, Walker JM (2007) Bridging the inferential gap: the electronic health record and clinical evidence. Health Aff (Millwood) 26: w181-w191
3. Kohane IS (2011) Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 12:417-428
4. Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C et al (2013) Electronic health records: new opportunities for clinical research. J Intern Med 274(6):547-560
5. Kukafka R, Ancker JS, Chan C, Chelico J, Khan S, Mortoti S et al (2007) Redesigning electronic health record systems to support public health. J Biomed Inform 40:398-409
6. Denny JC (2012) Chapter 13: Mining electronic health records in the genomics era. PLoS Comput Biol 8:e1002823
7. Bath P (2004) Data mining in health and medical information. Annu Rev Inform Sci Technol 38:331-369
8. van Bemmel JH, van Mulligen EM, Mons B, van Wijk M, Kors JA, van der Lei J (2006) Databases for knowledge discovery. Examples from biomedicine and health care. Int J Med Inform 75:257-267
9. Iavindrasana J, Cohen G, Depeursinge A, Muller H, Meyer R, Geissbuhler A (2009) Clinical data mining: a review. Yearb Med Inform:121-133
10. Murdoch TB, Detsky AS (2013) The inevitable application of big data to health care. JAMA 309:1351-1352
11. Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T et al (2011) Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 7:e1002141
12. Holmes AB, Hawson A, Liu F, Friedman C, Khiabanian H, Rabadan R (2011) Discovering disease associations by integrating electronic clinical data and medical literature. PLoS One 6:e21132
13. Hanauer DA, Rhodes DR, Chinnaiyan AM (2009) Exploring clinical associations using ‘-omics’ based enrichment analyses. PLoS One 4:e5203
14. Wilson AM, Thabane L, Holbrook A (2004) Application of data mining techniques in pharmacovigilance. Br J Clin Pharmacol 57: 127-134
15. Wang X, Hripcsak G, Markatou M, Friedman C (2009) Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 16:328-337
16. Harpaz R, Perez H, Chase HS, Rabadan R, Hripcsak G, Friedman C (2011) Biclustering of adverse drug events in the FDA’s spontaneous reporting system. Clin Pharmacol Ther 89:243-250
17. Wilke RA, Xu H, Denny JC, Roden DM, Krauss RM, McCarty CA et al (2011) The emerging role of electronic medical records in pharmacogenomics. Clin Pharmacol Ther 89:379-386
18. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17:37-54
19. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39:27-34
20. Hearst M (1999) Untangling text data mining. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on computational linguistics, pp 3-10
21. Zweigenbaum P, Demner-Fushman D, Yu H, Cohen KB (2007) Frontiers of biomedical text mining: current progress. Brief Bioinform 8:358-375
22. Institute of Medicine (2003) Key capabilities of an electronic health record system. National Academies Press, Washington, DC
23. National Institutes of Health National Center for Research Resources and MITRE Corporation (2006) Electronic health records overview. http://www.himss.org/files/HIMSSorg/content/files/Code%20180%20MITRE%20Key%20Components%20of%20 an%20EHR.pdf
24. ASTM Standard E1384 (2013) Standard guide for content and structure of the Electronic Health Record (EHR). ASTM International, West Conshohocken, PA
25. Carter J (2008) Electronic health records for clinicians and administrators: infrastructure and supporting technologies. In: Carter J (ed) Electronic health records, 2nd edn. American College of Physicians, Philadelphia, PA
26. MacKenzie SL, Wyatt MC, Schuff R, Tenenbaum JD, Anderson N (2012) Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey. J Am Med Inform Assoc 19: e119-e124
27. http://mimic.physionet.org?
28. Scott DJ, Lee J, Silva I, Park S, Moody GB, Celi LA et al (2013) Accessing the public MIMIC-II intensive care relational database for clinical research. BMC Med Inform Dec Mak 13:9
29. https://i2b2.org/NLP/DataSets?
30. Uzuner O, Luo Y, Szolovits P (2007) Evaluating the state-of-the-art in automatic de-identification. J Am Med Inform Assoc 14:550-563
31. Uzuner O, Goldstein I, Luo Y, Kohane I (2008) Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc 15:14-24
32. Uzuner O, Solti I, Cadag E (2010) Extracting medication information from clinical text. J Am Med Inform Assoc 17:514-518
33. Ohno-Machado L, Bafna V, Boxwala AA, Chapman BE, Chapman WW, Chaudhuri K et al (2012) iDASH: integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc 19:196-201
34. http://idash.ucsd.edu/data-repository-0
35. Ackoff R (1989) From data to wisdom. J Appl Syst Anal 16:3-9
36. Cao H, Markatou M, Melton GB, Chiang MF, Hripcsak G (2005) Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics. AMIA Annu Symp Proc:106-110
37. Cao H, Hripcsak G, Markatou M (2007) A statistical methodology for analyzing cooccurrence data from a large sample. J Biomed Inform 40:343-352
38. Chen ES, Hripcsak G, Xu H, Markatou M, Friedman C (2008) Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 15:87-98
39. Chen ES, Stetson PD, Lussier YA, Markatou M, Hripcsak G, Friedman C (2007) Detection of practice pattern trends through Natural Language Processing of clinical narratives and biomedical literature. AMIA Annu Symp Proc:120-124
40. Wang X, Hripcsak G, Friedman C (2009) Characterizing environmental and phenotypic associations using information theory and electronic health records. BMC Bioinforma 10(Suppl 9):S13
41. Wang X, Chase H, Markatou M, Hripcsak G, Friedman C (2010) Selecting information in electronic health records for knowledge acquisition. J Biomed Inform 43:595-601
42. Wright A, Chen ES, Maloney FL (2010) An automated technique for identifying associations between medications, laboratory results and problems. J Biomed Inform 43: 891-901
43. Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ et al (2011) A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 18:859-867
44. Doddi S, Marathe A, Ravi SS, Torney DC (2001) Discovery of association rules in medical data. Med Inform Internet Med 26: 25-33
45. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K et al (2010) PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26:1205-1210
46. Mullins IM, Siadaty MS, Lyman J, Scully K, Garrett CT, Miller WG et al (2006) Data mining and clinical data repositories: insights from a 667,000 patient data set. Comput Biol Med 36:1351-1377
47. Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R (2011) Mining health care administrative data with temporal association rules on hybrid events. Methods Inf Med 50: 166-179
48. 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. J Am Med Inform Assoc 20:e147-e154
49. Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG et al (2013) Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc 20(e2):e226-e231
50. http://www.phekb.org?
51. Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA et al (2013) The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 15(10):761-771
52. http://www.mtsamples.com/site/pages/sample. asp?type=97-Consult%20-%20History%20 and%20Phy.&sample=2063-Gen%20Med%20 Consult%20-%2049
53. Weiskopf NG, Weng C (2013) Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 20: 144-151
54. Rahm E, Do H (2000) Data cleaning: problems and current approaches. IEEE Data Eng Bull 23:3-13
55. http://www.w3.org/TR/NOTE-datetime
56. Bodenreider O (2004) The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 32:D267-D270
57. Post AR, Harrison JH Jr (2008) Temporal data mining. Clin Lab Med 28:83-100, vii
58. Carter C, Hamilton H (1995) A fast, on-line generalization algorithm for knowledge discovery. Appl Math Lett 8:5-11
59. http://knowledgemap.mc.vanderbilt.edu?research/content/phewas
60. http://www.hcup-us.ahrq.gov/toolssoftware?ccs/ccs.jsp
61. Liu H, Motoda H (1998) Feature extraction, construction and selection: a data mining perspective. Kluwer, Boston
62. Dunham MH (2003) Data mining introductory and advanced topics. Prentice Hall, Upper Saddle River, NJ
63. Sarkar IN (2013) Methods in biomedical informatics: a pragmatic approach, 1st edn. Academic, New York
64. Zupan B, Demsar J (2008) Open-source tools for data mining. Clin Lab Med 28:37-54, vi
65. http://www.kdnuggets.com/software?index.html
66. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining, 1st edn. Pearson Addison Wesley, Boston
67. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. Proceedings of the 20th International conference on very large data bases, pp 487-499
68. Hipp J, Guntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explor Newslett 2:58-64
69. Tan P, Kumar V, Srivastava J (2002) Selecting the right interestingness measure for association patterns. Proceedings of the 8th ACM SIGKDD International conference on knowledge discovery and data mining, pp 32-41
70. Ohsaki M, Abe H, Tsumoto S, Yokoi H, Yamaguchi T (2007) Evaluation of rule interestingness measures in medical knowledge discovery in databases. Artif Intell Med 41: 177-196
71. Hidalgo CA, Blumm N, Barabasi AL, Christakis NA (2009) A dynamic network approach for the study of human phenotypes. PLoS Comput Biol 5:e1000353
72. Harpaz R, Chase HS, Friedman C (2010) Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinforma 11(Suppl 9):S7
73. Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. KDD ‘99 Proceedings of the 5th ACM SIGKDD International conference on knowledge discovery and data mining, pp 337-341
74. http://www.graphviz.org?
75. http://www.cytoscape.org?
76. http://www.cs.waikato.ac.nz/ml/weka?
77. http://orange.biolab.si?
78. http://informatics.mc.vanderbilt.edu?archives/starchart
79. http://cerner.com?
80. http://www.epic.com?
81. http://medsphere.com/vista-to-openvista
82. http://www.hl7.org
83. Friedman C, Johnson S (2006) Natural language and text processing in biomedicine. In: Shortliffe E, Cimino JJ (eds) Biomedical informatics computer applications in health care and biomedicine, 3rd edn. Springer, New York
84. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF (2008) Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform:128-144
85. Cimino JJ (1996) Review paper: coding systems in health care. Methods Inf Med 35: 273-284
86. Cimino JJ, Zhu X (2006) The practical impact of ontologies on biomedical informatics. Yearb Med Inform:124-135
87. Friedman C (2000) A broad-coverage natural language processing system. Proc AMIA Symp:270-274
88. Friedman C, Hripcsak G, Shagina L, Liu H (1999) Representing information in patient reports using natural language processing and the extensible markup language. J Am Med Inform Assoc 6:76-87
89. Friedman C, Shagina L, Lussier Y, Hripcsak G (2004) Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc 11:392-402
90. Aronson AR, Lang FM (2010) An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 17:229-236
91. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC et al (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17:507-513
92. Zeng QT, Goryachev S, Weiss S, Sordo M, Murphy SN, Lazarus R (2006) Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Dec Mak 6:30
93. Kohane IS, Churchill SE, Murphy SN (2012) A translational engine at the national scale: informatics for integrating biology and the bedside. J Am Med Inform Assoc 19: 181-185
94. McMurry AJ, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J et al (2013) SHRINE: enabling nationally scalable multisite disease studies. PLoS One 8:e55811
95. Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR et al (2008) Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 84:362-369
96. Lowe HJ, Ferris TA, Hernandez PM, Weber SC (2009) STRIDE—an integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009:391-395
97. Chute CG, Beck SA, Fisk TB, Mohr DN (2010) The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. J Am Med Inform Assoc 17:131-135
98. Cimino JJ, Ayres EJ (2010) The clinical research data repository of the US National Institutes of Health. Stud Health Technol Inform 160:1299-1303
99. Payne P, Ervin D, Dhaval R, Borlawsky T, Lai A (2011) TRIAD: the Translational Research Informatics and Data Management Grid. Appl Clin Inform 2:331-344
100. Wylie JE, Mineau GP (2003) Biomedical databases: protecting privacy and promoting research. Trends Biotechnol 21:113-116
101. Malin B, Karp D, Scheuermann RH (2010) Technical and policy approaches to balancing patient privacy and data sharing in clinical and translational research. J Investig Med 58: 11-18
102. Krishna R, Kelleher K, Stahlberg E (2007) Patient confidentiality in the research use of clinical medical databases. Am J Public Health 97:654-658
103. Berman JJ (2002) Confidentiality issues for medical data miners. Artif Intell Med 26: 25-36
104. http://www.hhs.gov/ocr/privacy/index.html
105. Gunn PP, Fremont AM, Bottrell M, Shugarman LR, Galegher J, Bikson T (2004) The Health Insurance Portability and Accountability Act Privacy Rule: a practical guide for researchers. Med Care 42:321-327
106. Nosowsky R, Giordano TJ (2006) The Health Insurance Portability and Accountability Act of 1996 (HIPAA) privacy rule: implications for clinical research. Annu Rev Med 57:575-590
107. Meystre SM, Friedlin FJ, South BR, Shen S, Samore MH (2010) Automatic deidentification of textual documents in the electronic health record: a review of recent research. BMC Med Res Methodol 10:70
108. Kushida CA, Nichols DA, Jadrnicek R, Miller R, Walsh JK, Griffin K (2012) Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies. Med Care 50(Suppl): S82-S101
109. El Emam K, Jonker E, Arbuckle L, Malin B (2011) A systematic review of re-identification attacks on health data. PLoS One 6:e28071
110. Murphy SN, Gainer V, Mendis M, Churchill S, Kohane I (2011) Strategies for maintaining patient privacy in i2b2. J Am Med Inform Assoc 18(Suppl 1):i103-i108
111. Hammond WE (2005) The making and adoption of health data standards. Health Aff (Millwood) 24:1205-1213
112. Chen ES, Melton GB, Sarkar IN (2012) Translating standards into practice: experiences and lessons learned in biomedicine and health care. J Biomed Inform 45:609-612
113. http://www.who.int/classifications/icd/en?
114. http://www.ama-assn.org/go/cpt
115. http://loinc.org?
116. Vreeman DJ, McDonald CJ, Huff SM (2010) LOINC(R)—a universal catalog of individual clinical observations and uniform representation of enumerated collections. Int J Funct Inform Personal Med 3:273-291
117. http://www.nlm.nih.gov/research/umls?rxnorm?
118. Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R (2011) Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc 18:441-448
119. http://www.ihtsdo.org/snomed-ct?
120. http://www.nlm.nih.gov/mesh?
121. http://www.nlm.nih.gov/research/umls?
122. http://bioportal.bioontology.org?
123. Noy NF, Shah NH, Whetzel PL, Dai B, Dorf M, Griffith N et al (2009) BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 37: W170-W173