Computer Science > Computation and Language
arXiv:2603.09685 (cs)
[Submitted on 10 Mar 2026]
Title:Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records
Authors:Jacopo Vitale, David Della Morte, Luca Bacco, Mario Merone, Mark de Groot, Saskia Haitjema, Leandro Pecchia, Bram van Es
View a PDF of the paper titled Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records, by Jacopo Vitale and 7 other authors
View PDF
Abstract:To overcome the limitations of manual administrative coding in geriatric Cardiovascular Risk Management, this study introduces an automated classification framework leveraging unstructured Electronic Health Records (EHRs). Using a dataset of 3,482 patients, we benchmarked three distinct modeling paradigms on longitudinal Dutch clinical narratives: classical machine learning baselines, specialized deep learning architectures optimized for large-context sequences, and general-purpose generative Large Language Models (LLMs) in a zero-shot setting. Additionally, we evaluated a late fusion strategy to integrate unstructured text with structured medication embeddings and anthropometric data. Our analysis reveals that the custom Transformer architecture outperforms both traditional methods and generative \acs{llm}s, achieving the highest F1-scores and Matthews Correlation Coefficients. These findings underscore the critical role of specialized hierarchical attention mechanisms in capturing long-range dependencies within medical texts, presenting a robust, automated alternative to manual workflows for clinical risk stratification.
| Comments: | |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2603.09685 [cs.CL] |
| (or arXiv:2603.09685v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09685
Focus to learn more
arXiv-issued DOI via DataCite
|
Full-text links:
Access Paper:
- View PDF
- TeX Source
View a PDF of the paper titled Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records, by Jacopo Vitale and 7 other authors
Current browse context:
cs.CL
References & Citations
export BibTeX citation
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
Links to Code Toggle
Papers with Code (What is Papers with Code?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
