Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
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Computer Science > Machine Learning
arXiv:2606.10725 (cs)
[Submitted on 9 Jun 2026]
Title:Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
Authors:Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov, Nikita Khromov, Iaroslav Bespalov, Alexander Zolotarev, Kirill Grishchenkov, Ekaterina Ivanova, Miron Kuznetsov, Ilya Sochenkov, Elizaveta Panchenko, Artem Shelmanov, Dmitry V. Dylov
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Abstract:Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group. Most target long-term (5-10 year) rather than medium-term prediction. We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data.
Methods. Single-center retrospective study of electronic health records from the National Research Cardiology Center (Russia) for patients aged >=18 with CVD but without pre-existing AF, hospitalized more than once between January 2012 and May 2019. A custom NLP pipeline transformed unstructured discharge reports into 73 structured features, combining a rule-based parser with transformer-based NER. Using LightAutoML we built a full model (73 features), a simple model (reduced subset), and a linear model for a bedside risk score. Performance was assessed by ROC AUC, compared with CHARGE-AF, C2HEST, MHS, and HAVOC, and interpreted via SHAP.
Results. Of 80,576 records from 45,000 patients, 17,562 met inclusion criteria; 1,438 (8.19%) developed AF. The full model reached ROC AUC 0.735 (24-month) and 0.696 (entire follow-up); the simple model was nearly identical (0.725, 0.696). All non-linear models outperformed the four clinical risk scores (ROC AUC 0.53-0.64). The simple model uses 13 features and is named Pre-AF 13. SHAP identified age and left atrial volume as dominant predictors. A linear risk score (Pre-AF 9) stratified observed 24-month AF incidence from ~7% to 36%.
Conclusion. Interpretable ML models built from routinely collected EHR data identify high-AF-risk CVD patients, outperforming established clinical risk scores.
| Comments: | Main paper with appendix; 3 main figures, 3 supplementary figures, multiple tables. O. Shakhmatova and D. Kriukov contributed equally (co-first authors). E. Panchenko, A. Shelmanov, and D. V. Dylov are co-senior authors. Corresponding authors: O. Shakhmatova (this http URL@gmail.com) and D. V. Dylov (this http URL@skol.tech) |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.10725 [cs.LG] |
| (or arXiv:2606.10725v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10725
arXiv-issued DOI via DataCite (pending registration)
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