Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing

Benjamin, 2017, Heart disease and stroke statistics—2017 Update: a report from the American Heart Association, Circulation, 135, e146, 10.1161/CIR.0000000000000485

Diener, 2017, Secondary stroke prevention in cryptogenic stroke and embolic stroke of undetermined source (ESUS), Curr Neurol Neurosci Rep, 17, 64, 10.1007/s11910-017-0775-5

Adams, 1993, Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment, Stroke, 24, 35, 10.1161/01.STR.24.1.35

Ay, 2007, A computerized algorithm for etiologic classification of ischemic stroke: the causative classification of stroke system, Stroke, 38, 2979, 10.1161/STROKEAHA.107.490896

Amarenco, 2013, The ASCOD phenotyping of ischemic stroke (updated ASCO phenotyping), Cerebrovasc Dis, 36, 1, 10.1159/000352050

Gökçal, 2017, Etiological classification of ischemic stroke in young patients: a comparative study of TOAST, CCS, and ASCO, Acta Neurol Belg, 117, 643, 10.1007/s13760-017-0813-8

Marnane, 2010, Stroke subtype classification to mechanism-specific and undetermined categories by TOAST, A-S-C-O, and causative classification system: direct comparison in the North Dublin population stroke study, Stroke, 41, 1579, 10.1161/STROKEAHA.109.575373

Lin, 2017, Predicting domain-specific health-related quality of life using acute infarct volume, Stroke, 48, 1925, 10.1161/STROKEAHA.117.017094

Goldstein, 2001, Improving the reliability of stroke subgroup classification using the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria, Stroke, 32, 1091, 10.1161/01.STR.32.5.1091

Savova, 2010, Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications, J Am Med Inform Assoc, 17, 507, 10.1136/jamia.2009.001560

Abdi, 2010, Principal component analysis, WIREs Comp Stat, 2, 433, 10.1002/wics.101

Deerwester, 1990, Indexing by latent semantic analysis, J Am Soc Inform Sci, 41, 391, 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

Chen, 2016, XGBoost: a scalable tree boosting system, 785

Peterson, 2009, K-nearest neighbor, Scholarpedia, 4, 1883, 10.4249/scholarpedia.1883

Cortes, 1995, Support-vector networks, Mach Learning, 20, 273, 10.1007/BF00994018

Liaw, 2002, Classification and regression by randomForest, R News, 2, 18

Geurts, 2006, Extremely randomized trees, Mach Learning, 63, 3, 10.1007/s10994-006-6226-1

Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann Stat, 54, 1189, 10.1214/aos/1013203451

Džeroski, 2004, Is combining classifiers with stacking better than selecting the best one, Mach Learning, 54, 255, 10.1023/B:MACH.0000015881.36452.6e

Kohavi, 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, 1137

Dunn, 1961, Multiple comparisons among means, J Am Stat Assoc, 56, 52, 10.1080/01621459.1961.10482090

Meschia, 2006, Worrall BB and siblings with ischemic stroke study I. Interobserver agreement in the Trial of Org 10172 in Acute Stroke Treatment classification of stroke based on retrospective medical record review, J Stroke Cerebrovasc Dis, 15, 266, 10.1016/j.jstrokecerebrovasdis.2006.07.001

McArdle, 2014, Agreement between TOAST and CCS ischemic stroke classification: the NINDS SiGN study, Neurology, 83, 1653, 10.1212/WNL.0000000000000942

Agarwal, 2018, A natural language processing framework for assessing hospital readmissions for patients with COPD, IEEE J Biomed Health Inform, 22, 588, 10.1109/JBHI.2017.2684121

Cai, 2016, Natural language processing technologies in radiology research and clinical applications, Radiographics, 36, 176, 10.1148/rg.2016150080

Yaghi, 2017, Atrial cardiopathy: a mechanism of cryptogenic stroke, Exp Rev Cardiovasc Ther, 15, 591, 10.1080/14779072.2017.1355238