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Mining peripheral arterial disease cases from narrative clinical notes using natural language processing Naveed Afzal, PhD, Sunghwan Sohn, PhD, Sara Abram, MD, Christopher G. Scott, MS, Rajeev Chaudhry, MBBS, MPH, Hongfang Liu, PhD, Iftikhar J. Kullo, MD, Adelaide M. Arruda-Olson, MD, PhD Journal of Vascular Surgery Volume 65, Issue 6, Pages (June 2017) DOI: /j.jvs Copyright © 2016 The Authors Terms and Conditions
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Fig 1 Dataset description. PAD, Peripheral arterial disease; REP, Rochester Epidemiology Project. Journal of Vascular Surgery , DOI: ( /j.jvs ) Copyright © 2016 The Authors Terms and Conditions
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Fig 2 Study design. ABI, Ankle-brachial index; EHR, electronic health record; ID, identification; NLP, natural language processing. Journal of Vascular Surgery , DOI: ( /j.jvs ) Copyright © 2016 The Authors Terms and Conditions
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Fig 3 Peripheral arterial disease (PAD) concept visualization.
Journal of Vascular Surgery , DOI: ( /j.jvs ) Copyright © 2016 The Authors Terms and Conditions
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Fig 4 Accuracy of natural language processing (NLP) algorithm compared with billing code algorithms (simple model and full model) for ascertainment of peripheral arterial disease (PAD) status. Journal of Vascular Surgery , DOI: ( /j.jvs ) Copyright © 2016 The Authors Terms and Conditions
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