ArticleJournal of the American Heart Association2020
Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.
Article in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in pulmonary hypertension: a systematic review.European journal of medical research · 2025Pooled it
- Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.Frontiers in cardiovascular medicine · 2024Pooled it
- Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs).BMC medical informatics and decision making · 2025Article
- A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study.Journal of medical Internet research · 2024Article
- Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.JMIR public health and surveillance · 2024Article
- Using machine learning to improve anaphylaxis case identification in medical claims data.JAMIA open · 2024Article
- Development and validation of a code-based algorithm using in-hospital medical records to identify patients with pulmonary arterial hypertension in a French healthcare database.ERJ open research · 2024Article
- Resistin predicts disease severity and survival in patients with pulmonary arterial hypertension.Respiratory research · 2024Article
- Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.ESC heart failure · 2024Article
- Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities.Frontiers in drug safety and regulation · 2024Review
- Clinical evaluation of code-based algorithms to identify patients with pulmonary arterial hypertension in healthcare databases.Pulmonary circulation · 2024Article
- Article
- Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database.Pediatric blood & cancer · 2023Article
- Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol.Diagnostics (Basel, Switzerland) · 2022Article
- An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record.Respiratory research · 2022Article
- Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.Drug safety · 2022Review
- Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension.Circulation research · 2022Review
- Review
- Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.Journal of the American Heart Association · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and evaluated claims-based case ascertainment algorithms for pulmonary hypertension (PH), comparing conventional decision rules with state-of-the-art machine-learning approaches. Methods and Results We analyzed an electronic health record-Medicare linked database from two large academic tertiary care hospitals (years 2007-2013). Electronic health record charts were reviewed to form a gold standard cohort of patients with (n=386) and without PH (n=164). Using health encounter data captured in Medicare claims (including patients' demographics, diagnoses, medications, and procedures), we developed and compared 2 approaches for identifying patients with PH: decision rules and machine-learning algorithms using penalized lasso regression, random forest, and gradient boosting machine. The most optimal rule-based algorithm-having ≥3 PH-related healthcare encounters and having undergone right heart catheterization-attained an area under the receiver operating characteristic curve of 0.64 (sensitivity, 0.75; specificity, 0.48). All 3 machine-learning algorithms outperformed the most optimal rule-based algorithm (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.