Kukafka R, et al. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007;40(4):398–409.
Chaudhry B, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742–52.
Afzal Z, et al. Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases. Pharmacoepidemiol Drug Saf. 2013;22(8):826–33.
Schuemie MJ, et al. Automating classification of free-text electronic health records for epidemiological studies. Pharmacoepidemiol Drug Saf. 2012;21(6):651–8.
Valkhoff VE, et al. Validation study in four health-care databases: upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk. J Clin Epidemiol. 2014;67(8):921–31.
Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13(6):395–405.
Ho ML, et al. The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitus. J Eval Clin Pract. 2012;18(3):606–11.
Klompas M, et al. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36(4):914–21.
Kudyakov R, et al. Electronic health record use to classify patients with newly diagnosed versus preexisting type 2 diabetes: infrastructure for comparative effectiveness research and population health management. Popul Health Manag. 2012;15(1):3–11.
Lawrence JM, et al. Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization. Am J Epidemiol. 2014;179(1):27–38.
Fan J, et al. Billing code algorithms to identify cases of peripheral artery disease from administrative data. J Am Med Inform Assoc. 2013;20:e349–54.
Hammad TA, et al. Determining the predictive value of Read/OXMIS codes to identify incident acute myocardial infarction in the General Practice Research Database. Pharmacoepidemiol Drug Saf. 2008;17(12):1197–201.
Kottke TE, Baechler CJ. An algorithm that identifies coronary and heart failure events in the electronic health record. Prev Chronic Dis. 2013;10:E29. doi:10.5888/pcd10.120097.
Murff HJ, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA. 2011;306(8):848–55.
Fleet JL, et al. Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes. BMC Nephrol. 2013;14:81.
Dregan A, et al. Utility of electronic patient records in primary care for stroke secondary prevention trials. BMC Public Health. 2011;11:86.
Tu K, et al. Validity of administrative data for identifying patients who have had a stroke or transient ischemic attack using EMRALD as a reference standard. Can J Cardiol. 2013;29(11):1388–94.
Churpek MM, et al. Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*. Crit Care Med. 2014;42(4):841–8.
Fan J, et al. Billing code algorithms to identify cases of peripheral artery disease from administrative data. J Am Med Inform Assoc. 2013;20(e2):e349–54.
Jensen PN, et al. A systematic review of validated methods for identifying atrial fibrillation using administrative data. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 1):141–7.
Kadhim-Saleh A, et al. Validation of the diagnostic algorithms for 5 chronic conditions in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN): a Kingston Practice-based Research Network (PBRN) report. J Am Board Fam Med. 2013;26(2):159–67.
Vijayakrishnan R, et al. Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record. J Card Fail. 2014;20(7):459–64.
Savova GK, et al. Mayo clinic NLP system for patient smoking status identification. J Am Med Inform Assoc. 2008;15(1):25–8.
Sohn S, Savova GK. Mayo clinic smoking status classification system: extensions and improvements. AMIA Annu Symp Proc. 2009;2009:619–23.
Uzuner O, et al. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc. 2008;15(1):14–24.
Wu CY, et al. Evaluation of smoking status identification using electronic health records and open-text information in a large mental health case register. PLoS ONE. 2013;8(9):e74262.
Hivert MF, et al. Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records. BMC Health Serv Res. 2009;9:170.
Alsara A, et al. Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury. Mayo Clin Proc. 2011;86(5):382–8.
Green BB, et al. Using body mass index data in the electronic health record to calculate cardiovascular risk. Am J Prev Med. 2012;42(4):342–7.
Persell SD, et al. Electronic health record-based cardiac risk assessment and identification of unmet preventive needs. Med Care. 2009;47(4):418–24.
Richards A, Cheng EM. Stroke risk calculators in the era of electronic health records linked to administrative databases. Stroke. 2013;44(2):564–9.
Deleger L, Grouin C, Zweigenbaum P. Extracting medical information from narrative patient records: the case of medication-related information. J Am Med Inform Assoc. 2010;17(5):555–8.
Fung KW, Jao CS, Demner-Fushman D. Extracting drug indication information from structured product labels using natural language processing. J Am Med Inform Assoc. 2013;20(3):482–8.
Levin MA, et al. Extraction and mapping of drug names from free text to a standardized nomenclature. AMIA Annu Symp Proc. 2007;2007:438–42.
Xu H, et al. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc. 2010;17(1):19–24.
Sai K, et al. Development of a detection algorithm for statin-induced myopathy using electronic medical records. J Clin Pharm Ther. 2013;38(3):230–5.
Skentzos S, et al. Structured vs. unstructured: factors affecting adverse drug reaction documentation in an EMR repository. AMIA Annu Symp Proc. 2011;2011:1270–9.
Suissa S, Garbe E. Primer: administrative health databases in observational studies of drug effects–advantages and disadvantages. Nat Clin Pract Rheumatol. 2007;3(12):725–32.
Coloma PM, et al. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf. 2011;20(1):1–11.
Coloma PM, et al. Drug-induced acute myocardial infarction: identifying ‘prime suspects’ from electronic healthcare records-based surveillance system. PLoS ONE. 2013;8(8):e72148.
Assaf AR, et al. Coronary heart disease surveillance: field application of an epidemiologic algorithm. J Clin Epidemiol. 2000;53(4):419–26.
Cutrona SL, et al. Validation of acute myocardial infarction in the Food and Drug Administration’s Mini-Sentinel program. Pharmacoepidemiol Drug Saf. 2013;22(1):40–54.
Kottke TE, Baechler CJ, Parker ED. Accuracy of heart disease prevalence estimated from claims data compared with an electronic health record. Prev Chronic Dis. 2012;9:E141.
Tu K, et al. Validation of physician billing and hospitalization data to identify patients with ischemic heart disease using data from the Electronic Medical Record Administrative data Linked Database (EMRALD). Can J Cardiol. 2010;26(7):e225–8.
Gulliford MC, et al. Selection of medical diagnostic codes for analysis of electronic patient records. Application to stroke in a primary care database. PLoS ONE. 2009;4(9):e7168.
Allen LA, et al. Performance of claims-based algorithms for identifying heart failure and cardiomyopathy among patients diagnosed with breast cancer. Med Care. 2014;52(5):e30–8.
Loehr LR, et al. Classification of acute decompensated heart failure: an automated algorithm compared with a physician reviewer panel: the Atherosclerosis Risk in Communities study. Circ Heart Fail. 2013;6(4):719–26.
Lee DS, et al. Comparison of coding of heart failure and comorbidities in administrative and clinical data for use in outcomes research. Med Care. 2005;43(2):182–8.
Rosenman M, et al. Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory. J Am Med Inform Assoc. 2014;21(2):345–52.
Saczynski JS, et al. A systematic review of validated methods for identifying heart failure using administrative data. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 1):129–40.
Karmali KN, et al. A systematic examination of the 2013 ACC/AHA pooled cohort risk assessment tool for atherosclerotic cardiovascular disease. J Am Coll Cardiol. 2014;64(10):959–68.
D’Agostino RB Sr, et al. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286(2):180–7.