ReviewHeart failure reviews2025
Clinical and research applications of natural language processing for heart failure.
Review in Heart failure reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Clinical endpoints in pragmatic heart failure trials: From data collection to clinical endpoint classification.ESC heart failure · 2025Review
- Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects.Frontiers in nutrition · 2025Review
- The role of redox-active iron, copper, manganese, and redox-inactive zinc in toxicity, oxidative stress, and human diseases.EXCLI journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural language processing (NLP) is a burgeoning field of machine learning/artificial intelligence that focuses on the computational processing of human language. Researchers and clinicians are using NLP methods to advance the field of medicine in general and in heart failure (HF), in particular, by processing vast amounts of previously untapped semi-structured and unstructured textual data in electronic health records. NLP has several applications to clinical research, including dramatically improving processes for cohort assembly, disease phenotyping, and outcome ascertainment, among others. NLP also has the potential to improve direct clinical care through early detection, accurate diagnosis, and evidence-based management of patients with HF. In this state-of-the-art review, we present a general overview of NLP methods and review clinical and research applications in the field of HF. We also propose several potential future directions of this emerging and rapidly evolving technological breakthrough.
Indexed as
Identifiers
39699708What OpenQuestion holds
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.