Article in Circulation. Arrhythmia and electrophysiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
17 authors.
Ruibin Feng *Department of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-0777-8195
Kelly A Brennan *Department of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-7698-7353
Zahra AziziDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-7897-0934
Jatin GoyalDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0009-0008-9593-8689
Brototo DebDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0001-8800-0002
Hui Ju ChangDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-3734-0493
Prasanth GanesanDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-1885-0690
Paul CloptonDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-0642-0861
Maxime PedronDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).
Samuel Ruipérez-CampilloDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-5425-4175
Yaanik B DesaiDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0009-0000-4685-4739
Hugo De LarochellièreDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-6816-1033
Tina BaykanerDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0002-0476-2972
Marco V PerezDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0003-2746-0202
Miguel RodrigoDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).
Albert J RogersDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0001-6585-534X
Sanjiv M NarayanDepartment of Medicine, Stanford University, CA (R.F., K.A.B., Z.A., J.G., B.D., H.J.C., P.G., P.C., M. Pedron, S.R.-C., Y.B.D., H.D.L., T.B., M. V. P, M.R., A.J.R., S.M.N.).ORCID 0000-0001-7552-5053
Funding
The Dynamics of Human Atrial FibrillationR01HL083359 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Sanjiv M Narayan · 2008 to 2026
$7.9M
Machine Learning in Atrial FibrillationR01HL149134 · NHLBI · STANFORD UNIVERSITY · PI NARAYAN, SANJIV M · 2020 to 2024
$3.8M
Machine Learning for Ventricular ArrhythmiasR01HL162260 · NHLBI · STANFORD UNIVERSITY · PI Sanjiv M Narayan · 2023 to 2026
$2.5M
Deep Neural Networks To Treat Atrial FibrillationK23HL145017 · NHLBI · STANFORD UNIVERSITY · PI BAYKANER, TINA · 2019 to 2023
$968k
Novel Atrial Fibrillation Phenotypes Defined by Functional-Anatomical, Machine-Learned ClassificationsF32HL144101 · NHLBI · STANFORD UNIVERSITY · PI ROGERS, ALBERT JOSEPH · 2018 to 2019
backgroundLarge language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT) excel at interpreting unstructured data from public sources, yet are limited when responding to queries on private repositories, such as electronic health records (EHRs). We hypothesized that prompt engineering could enhance the accuracy of LLMs for interpreting EHR data without requiring domain knowledge, thus expanding their utility for patients and personalized diagnostics.
methodsWe designed and systematically tested prompt engineering techniques to improve the ability of LLMs to interpret EHRs for nuanced diagnostic questions, referenced to a panel of medical experts. In 490 full-text EHR notes from 125 patients with prior life-threatening heart rhythm disorders, we asked GPT-4-turbo to identify recurrent arrhythmias distinct from prior events and tested 220 563 queries. To provide context, results were compared with rule-based natural language processing and Bidirectional Encoder Representations from Transformer-based language models. Experiments were repeated for 2 additional LLMs.
resultsIn an independent hold-out set of 389 notes, GPT-4-turbo had a balanced accuracy of 64.3%±4.7% out-of-the-box at baseline. This increased when asking GPT-4-turbo to provide a rationale for its answers, a structured data output, and in-context exemplars, to a balanced accuracy of 91.4%±3.8% (
conclusionsThe use of prompt engineering strategies enables LLMs to identify clinical end points from EHRs with an accuracy that surpassed natural language processing and approximated experts, yet without the need for expert knowledge. These approaches could be applied to LLM queries for other domains, to facilitate automated analysis of nuanced data sets with high accuracy by nonexperts.
Indexed as
Arrhythmias, CardiacArtificial IntelligenceElectronic Health RecordsNatural Language ProcessingFemaleGenerative Artificial IntelligenceHumansMalePredictive Value of TestsRecurrenceartificial intelligenceclinical decision supportelectronic health recordsnatural language processingventricular tachycardia
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.
Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence. · full record | OpenQuestion