Ascertainment of asthma prognosis using natural language processing from electronic medical records.

[1]  E. Ryu,et al.  What accounts for the association between late preterm births and risk of asthma? , 2017, Allergy and asthma proceedings.

[2]  M. Sears.  Predicting asthma outcomes. , 2015, The Journal of allergy and clinical immunology.

[3]  C. Robertson,et al.  Outcomes of childhood asthma to the age of 50 years. , 2014, The Journal of allergy and clinical immunology.

[4]  K. E. Ravikumar,et al.  Automated chart review for asthma cohort identification using natural language processing: an exploratory study. , 2013, Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology.

[5]  Christopher G Chute,et al.  An Information Extraction Framework for Cohort Identification Using Electronic Health Records , 2013, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[6]  James Tonascia,et al.  Predictors of remitting, periodic, and persistent childhood asthma. , 2010, The Journal of allergy and clinical immunology.

[7]  Third Expert Panel on theDiagnosis,et al.  Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma , 1997 .

[8]  Mir H. Ali,et al.  Natural Language Processing for Asthma Ascertainment in Different Practice Settings. , 2018, The journal of allergy and clinical immunology. In practice.

[9]  Hongfang Liu,et al.  Staggered NLP-assisted refinement for Clinical Annotations of Chronic Disease Events , 2016, LREC.

[10]  Expert Panel Report 3 (EPR-3): Guidelines for the Diagnosis and Management of Asthma-Summary Report 2007. , 2007, The Journal of allergy and clinical immunology.