Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients

Centers for Medicare & Medicaid Services (US). 42 CFR Parts 412, 413, 422 et al (2010) Medicare and Medicaid Programs; Electronic Health Records Incentive Program; Final Rule. Fed Regist 75(144):44314–44588

Vest JR, Zhao H, Jaspserson J, Gamm LD, Ohsfeldt RL (2011) Factors motivating and affecting health information exchange usage. J Am Med Inform Assoc 18(2):143–149

Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW (2007) Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. Jama 297(8):831–841

Tafti AP, et al (2016) OCR as a service: an experimental evaluation of Google Docs OCR, Tesseract, ABBYY FineReader, and Transym, in Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12–14, 2016, Proceedings, Part I, G. Bebis, et al., editors, Springer International Publishing: Cham. p. 735–746

Rasmussen LV (2014) The electronic health record for translational research. J Cardiovasc Transl Res 7(6):607–614

Rasmussen LV, Peissig PL, McCarty CA, Starren J (2012) Development of an optical character recognition pipeline for handwritten form fields from an electronic health record. J Am Med Inform Assoc: JAMIA 19(e1):e90–e95

Biondich PG, et al. (2002) A modern optical character recognition system in a real world clinical setting: some accuracy and feasibility observations. Proceedings of the AMIA Symposium: p. 56–60

Peissig PL, Rasmussen LV, Berg RL, Linneman JG, McCarty CA, Waudby C, Chen L, Denny JC, Wilke RA, Pathak J, Carrell D, Kho AN, Starren JB (2012) Importance of multi-modal approaches to effectively identify cataract cases from electronic health records. J Am Med Inform Assoc 19(2):225–234

Bussmann H et al (2006) Hybrid data capture for monitoring patients on highly active antiretroviral therapy (HAART) in urban Botswana. Bull World Health Organ 84(2):127–131

Sauer BC, et al (2016) Performance of a Natural Language Processing (NLP) tool to extract pulmonary function test (PFT) reports from structured and semistructured Veteran Affairs (VA) data. eGEMs. 4(1)

Todd J, Richards B, Vanstone B, Gepp A (2018) Text mining and automation for processing of patient referrals. Appl Clin Inf 9(01):232–237

Biron P, Metzger MH, Pezet C, Sebban C, Barthuet E, Durand T (2014) An information retrieval system for computerized patient records in the context of a daily hospital practice: the example of the Léon Bérard Cancer Center (France). Appl Clin Inform 5(1):191–205

Keysers D et al (2003) Statistical framework for model-based image retrieval in medical applications. J Electron Imaging 12(1):59–68

Wang Y et al (2017) Clinical information extraction applications: a literature review. J Biomed Inform

Friedman C, Alderson PO, Austin JHM, Cimino JJ, Johnson SB (1994) A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1(2):161–174

Aronson AR, Lang F-M (2010) An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 17(3):229–236

Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17(5):507–513

Liu H et al. (2013) An information extraction framework for cohort identification using electronic health records. AMIA Summits on Translational Science Proceedings, 2013: p. 149

Liu H, Friedman C (2004) CliniViewer: a tool for viewing electronic medical records based on natural language processing and XML. Stud Health Technol Inform 107(Pt 1):639–643

Hallett C (2008) Multi-modal presentation of medical histories. in Proceedings of the 13th international conference on Intelligent user interfaces. ACM

Hirsch JS, Tanenbaum JS, Lipsky Gorman S, Liu C, Schmitz E, Hashorva D, Ervits A, Vawdrey D, Sturm M, Elhadad N (2014) HARVEST, a longitudinal patient record summarizer. J Am Med Inform Assoc 22(2):263–274

Bashyam V, Hsu W, Watt E, Bui AAT, Kangarloo H, Taira RK (2009) Problem-centric organization and visualization of patient imaging and clinical data. Radiographics 29(2):331–343

Pivovarov R, Elhadad N (2015) Automated methods for the summarization of electronic health records. J Am Med Inform Assoc 22(5):938–947

Grouin C and Zweigenbaum P (2013) Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches. in MedInfo

Heurix J, Fenz S, Rella A, Neubauer T (2016) Recognition and pseudonymisation of medical records for secondary use. Med Biol Eng Comput 54(2–3):371–383

Zuccon G et al. (2012) The impact of OCR accuracy on automated cancer classification of pathology reports. in HIC

Yadav K, Sarioglu E, Smith M, Choi HA (2013) Automated outcome classification of emergency department computed tomography imaging reports. Acad Emerg Med 20(8):848–854

Cui L et al (2012) EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. in AMIA Annual Symposium Proceedings. American Medical Informatics Association

Li X, Hu G, Teng X, Xie G (2015) Building structured personal health records from photographs of printed medical records. AMIA Ann Symp Proc 2015:833–842

Smith R (2007) An Overview of the Tesseract OCR Engine, in Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02, IEEE Computer Society. p. 629–633

Wang L et al. (2017) Discovering adverse drug events combining spontaneous reports with electronic medical records: a case study of conventional DMARDs and biologics for rheumatoid arthritis. AMIA Summits on Translational Science Proceedings, 2017: p. 95

Torii M, Wagholikar K, Liu H (2011) Using machine learning for concept extraction on clinical documents from multiple data sources. J Am Med Inform Assoc 18(5):580–587

Rector A, Rogers J, Bittner T (2006) Granularity, scale and collectivity: when size does and does not matter. J Biomed Inform 39(3):333–349

Kumar A, Smith B, Novotny DD (2004) Biomedical informatics and granularity. Comparative and functional genomics 5(6–7):501–508

McInnes BT, Pedersen T, and Pakhomov SV (2009) UMLS-Interface and UMLS-Similarity: open source software for measuring paths and semantic similarity. in AMIA Annual Symposium Proceedings. American Medical Informatics Association

Pedersen T, Pakhomov SVS, Patwardhan S, Chute CG (2007) Measures of semantic similarity and relatedness in the biomedical domain. J Biomed Inform 40(3):288–299

Moon S, Samudrala S, Liu S, Shellum JL, Ommen S, Nishimura RA, Liu H, Arruda-Olson A (2018) Automated identification of sudden death risk phenotypes from electronic health records of patients with hypertrophic cardiomyopathy. in American College of Cardiology 2018

Sohn S, Wang Y, Wi CI, Krusemark EA, Ryu E, Ali MH, Juhn YJ, Liu H (2017) Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions. J Am Med Inform Assoc 25(3):353–359

Van Such M et al (2017) Extent of diagnostic agreement among medical referrals. J Eval Clin Pract. https://doi.org/10.1111/jep.12747

Shen F, Wang L, Liu H (2017) Using human phenotype ontology for phenotypic analysis of clinical notes. Stud Health Technol Inform 245:1285–1285