Locke S et al (2021) Natural language processing in medicine: a review. Trends Anaesth Crit Care 38:4–9
Falter M et al (2024) Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease. Eur Heart J - Digit Health 5:229–234
Article PubMed PubMed Central Google Scholar
Bates BA et al (2023) Validity of International Classification of Diseases (ICD)-10 diagnosis codes for identification of acute heart failure hospitalization and heart failure with reduced versus preserved ejection fraction in a national Medicare sample. Circ Cardiovasc Qual Outcomes 16(2):e009078
Mahesri M et al (2021) External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure. PLoS ONE 16(6):e0252903
Article CAS PubMed PubMed Central Google Scholar
Venkatesh KP, Raza MM, Kvedar JC (2023) Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med 6(1):16
Article PubMed PubMed Central Google Scholar
McCormick N et al (2014) Validity of heart failure diagnoses in administrative databases: a systematic review and meta-analysis. PLoS ONE 9(8):e104519
Article PubMed PubMed Central Google Scholar
Kong HJ (2019) Managing unstructured big data in healthcare system. Healthc Inform Res 25(1):1–2
Article PubMed PubMed Central Google Scholar
Shah RU, Rumsfeld JS (2017) Big data in cardiology. Eur Heart J 38(24):1865–1867
Article PubMed PubMed Central Google Scholar
Sandhu AT et al (2024) Clinical impact of routine assessment of patient-reported health status in heart failure clinic: the PRO-HF trial. Circulation 149(22):1717–1728
Article CAS PubMed Google Scholar
Spertus JA et al (2020) Interpreting the Kansas City Cardiomyopathy Questionnaire in clinical trials and clinical care: JACC state-of-the-art review. J Am Coll Cardiol 76(20):2379–2390
Greene SJ et al (2021) Comparison of New York Heart Association class and patient-reported outcomes for heart failure with reduced ejection fraction. JAMA Cardiol 6(5):522–531
Article PubMed PubMed Central Google Scholar
Reading Turchioe M et al (2022) Systematic review of current natural language processing methods and applications in cardiology. Heart 108(12):909–916
Tohka J, van Gils M (2021) Evaluation of machine learning algorithms for health and wellness applications: a tutorial. Comput Biol Med 132:104324
Ahmad FS et al (2022) Advances in machine learning approaches to heart failure with preserved ejection fraction. Heart Fail Clin 18(2):287–300
Article PubMed PubMed Central Google Scholar
Khera R (2021) Digital cardiovascular epidemiology—ushering in a new era through computational phenotyping of cardiovascular disease. JAMA Netw Open 4(11):e2135561–e2135561
Ambrosy AP et al (2021) A natural language processing-based approach for identifying hospitalizations for worsening heart failure within an integrated health care delivery system. JAMA Netw Open 4(11):e2135152
Article PubMed PubMed Central Google Scholar
Pakhomov SV, Buntrock J, Chute CG (2005) Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier. J Biomed Inform 38(2):145–153
Pakhomov S et al (2007) Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care 13(6 Part 1):281–8
Jonnalagadda SR et al (2017) Text mining of the electronic health record: an information extraction approach for automated identification and subphenotyping of HFpEF patients for clinical trials. J Cardiovasc Transl Res 10(3):313–321
Hamilton SA et al (2024) Applying natural language processing to identify emergency department and observation encounters for worsening heart failure. ESC Heart Fail 11:2542–2545
Article PubMed PubMed Central Google Scholar
Cunningham JW et al (2023) Natural language processing for adjudication of heart failure in the electronic health record. JACC Heart Fail 11(7):852–854
Article CAS PubMed PubMed Central Google Scholar
Cunningham JW et al (2023) Natural language processing for adjudication of heart failure hospitalizations in a multi-center clinical trial. medRxiv [Preprint]. https://doi.org/10.1101/2023.08.17.23294234
Nagamine T et al (2020) Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data. Sci Rep 10(1):21340
Article CAS PubMed PubMed Central Google Scholar
Nagamine T et al (2022) Data-driven identification of heart failure disease states and progression pathways using electronic health records. Sci Rep 12(1):17871
Article CAS PubMed PubMed Central Google Scholar
Khan MS et al (2023) Artificial intelligence and heart failure: a state-of-the-art review. Eur J Heart Fail 25(9):1507–1525
Alhussain K et al (2021) Identifying knowledge gaps in heart failure research among women using unsupervised machine-learning methods. Future Cardiol 17(7):1215–1224
Article CAS PubMed PubMed Central Google Scholar
Wu J et al (2024) Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction. Eur J Heart Fail 26(2):302–310
Article CAS PubMed Google Scholar
Wang Y et al (2015) NLP based congestive heart failure case finding: a prospective analysis on statewide electronic medical records. Int J Med Inform 84(12):1039–1047
Baclic O et al (2020) Challenges and opportunities for public health made possible by advances in natural language processing. Can Commun Dis Rep 46(6):161–168
Article PubMed PubMed Central Google Scholar
Bozkurt B et al (2021) Universal definition and classification of heart failure: a report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure. J Cardiac Fail 27(4):387–413
Nargesi AA et al (2024) Automated identification of heart failure with reduced ejection fraction using deep learning-based natural language processing. JACC Heart Fail. https://doi.org/10.1016/j.jchf.2024.08.012
Kang Y, Hurdle J (2020) Predictive model for risk of 30-day rehospitalization using a natural language processing/machine learning approach among Medicare patients with heart failure. J Cardiac Fail 26(10):S5
Parikh RV et al (2023) Developing clinical risk prediction models for worsening heart failure events and death by left ventricular ejection fraction. J Am Heart Assoc 12(19):e029736
Article PubMed PubMed Central Google Scholar
Topaz M et al (2017) Studying associations between heart failure self-management and rehospitalizations using natural language processing. West J Nurs Res 39(1):147–165
Eggerth A et al (2020) Natural language processing for detecting medication-related notes in heart failure telehealth patients. Stud Health Technol Inform 270:761–765
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