TY - JOUR
T1 - Text Mining of the Electronic Health Record
T2 - An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials
AU - Jonnalagadda, Siddhartha R.
AU - Adupa, Abhishek K.
AU - Garg, Ravi P.
AU - Corona-Cox, Jessica
AU - Shah, Sanjiv J.
N1 - Publisher Copyright:
© 2017, Springer Science+Business Media New York.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subtypes of heterogeneous clinical syndromes (such as heart failure with preserved ejection fraction [HFpEF]), we need prescreening systems that are able to automate data extraction and decision-making tasks. However, a major obstacle is the vast amount of unstructured free-form text in medical records. Here we describe an information extraction-based approach that automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria using a rule-based system to determine which patients qualify for a major HFpEF clinical trial (PARAGON). We show that we can achieve a sensitivity and positive predictive value of 0.95 and 0.86, respectively. Our open-source algorithm could be used to efficiently identify and subphenotype patients with HFpEF and other disorders.
AB - Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subtypes of heterogeneous clinical syndromes (such as heart failure with preserved ejection fraction [HFpEF]), we need prescreening systems that are able to automate data extraction and decision-making tasks. However, a major obstacle is the vast amount of unstructured free-form text in medical records. Here we describe an information extraction-based approach that automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria using a rule-based system to determine which patients qualify for a major HFpEF clinical trial (PARAGON). We show that we can achieve a sensitivity and positive predictive value of 0.95 and 0.86, respectively. Our open-source algorithm could be used to efficiently identify and subphenotype patients with HFpEF and other disorders.
KW - Clinical trials
KW - Information extraction
KW - Natural language processing
KW - Precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85020169156&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020169156&partnerID=8YFLogxK
U2 - 10.1007/s12265-017-9752-2
DO - 10.1007/s12265-017-9752-2
M3 - Article
C2 - 28585184
AN - SCOPUS:85020169156
VL - 10
SP - 313
EP - 321
JO - Journal of Cardiovascular Translational Research
JF - Journal of Cardiovascular Translational Research
SN - 1937-5387
IS - 3
ER -