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Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials

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Abstract

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.

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Author information

Author notes

  1. Siddhartha R. Jonnalagadda

    Present address: Microsoft Corporation, 555 110th Ave NE, Bellevue, WA, 98004, USA

Authors and Affiliations

  1. Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA

    Siddhartha R. Jonnalagadda, Abhishek K. Adupa & Ravi P. Garg

  2. Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA

    Jessica Corona-Cox & Sanjiv J. Shah

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  1. Siddhartha R. Jonnalagadda
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Corresponding author

Correspondence to Siddhartha R. Jonnalagadda.

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Funding Sources

This work was funded by the National Library of Medicine: R00LM011389 and R01LM011416 (to S.R.J.), and an investigator-initiated study grant from Novartis. S.J.S. is also supported by grants from the National Institutes of Health (R01 HL107577 and R01 HL127028). The authors acknowledge Prasanth Nannapaneni for his valuable ideas on extracting information from the electronic health record.

Conflicts of Interest

Siddhartha R. Jonnalagadda is currently an employee of Microsoft Corporation.

Abhishek K. Adupa declares that he has no conflict of interest.

Ravi P. Garg declares that he has no conflict of interest.

Jessica Corona-Cox declares that she has no conflict of interest.

Sanjiv J. Shah reports receiving consulting fees from Novartis.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was waived for this study by the Northwestern University Institutional Review Board because the study only involved retrospective chart review.

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Jonnalagadda, S.R., Adupa, A.K., Garg, R.P. et al. Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials. J. of Cardiovasc. Trans. Res. 10, 313–321 (2017). https://doi.org/10.1007/s12265-017-9752-2

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  • DOI: https://doi.org/10.1007/s12265-017-9752-2

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