AI Learns to Predict Heart Disease Across Hospitals Without Sharing Patient Data

Heart disease remains one of the world’s leading causes of illness and death, and the risk prediction tools that doctors rely on, from the Pooled Cohort Equations to QRISK3 and SCORE2, are typically built from data held at a single institution or pooled centrally. That centralization creates a tension at the heart of modern medical AI: the more diverse the data a model sees, the better it tends to generalize, yet privacy regulations such as the General Data Protection Regulation classify health records as special-category personal data and restrict their movement across organizational boundaries. A new study from researchers at Erasmus MC University Medical Center Rotterdam, the Netherlands eScience Center, and University Medical Center Utrecht, published in Frontiers in Applied Mathematics and Statistics, reports that a machine learning technique called federated learning can break through this impasse, improving cardiovascular risk prediction across two very different Dutch cohorts without a single patient record ever leaving its home institution.

Federated learning inverts the usual logic of data science. Instead of shipping raw data to a central server, each participating site trains a model on its own local data and transmits only the resulting model parameter updates, such as learned network weights, to an aggregator that combines them into a shared global model. In the study, two data nodes, one hosting the Lifelines cohort and the other the Rotterdam Study, ran local training independently on the Vantage6 federated learning platform, while a separate aggregator node computed the combined weights using Federated Averaging, or FedAvg, a method that weights each site’s contribution by its number of training samples. The exchanged weights were encrypted end-to-end between the participating organizations so that even the central server could not read them.

The two cohorts involved were strikingly different, and that heterogeneity is precisely what makes the result noteworthy. Lifelines is a large population-based study of healthy aging in the northern Netherlands, contributing 148,230 eligible participants with cardiovascular outcomes derived from self-reported questionnaires. The Rotterdam Study, begun in 1990 in Rotterdam’s Ommoord district, contributed a much smaller cohort of 10,155 participants, but its outcomes were identified through continuous digital linkage with hospital discharge records and general practitioner files, providing exact event dates and complete follow-up. In Lifelines, event dates for heart attack, stroke, or heart failure had to be approximated as the midpoint between the last event-free assessment and the assessment at which the event was first reported, introducing imprecision in both the event indicator and the timing.

Before any learning could begin, the researchers had to solve a more mundane but formidable problem: making the two datasets speak the same language. They used the MyDigiTwin data harmonization pipeline, which converts raw tabular cohort data into patient-centered Common Data Format files, applies dataset-specific mapping rules to produce HL7 FHIR-compliant resources following the ZIB profile used in Dutch personal health environments, and then flattens those resources into the tabular structure the federated algorithm requires. Crucially, each cohort was harmonized independently at its own site, and no raw data were transferred between sites at any stage. The final predictor set comprised 12 variables consistently available in both cohorts: sex, age, systolic and diastolic blood pressure, HDL, LDL, and total cholesterol, estimated glomerular filtration rate, creatinine, smoking status, prevalent type 2 diabetes, and prevalent hypertension.

The prediction model itself was DeepSurv, a deep learning extension of the classical Cox proportional hazards model that replaces the linear risk score with a feed-forward neural network, allowing non-linear associations between risk factors and time-to-event outcomes to be learned from data. The network was deliberately compact, with an input layer of 12 neurons, two hidden layers of 16 neurons each, ReLU activations, batch normalization, and dropout of 0.20, amounting to roughly 500 trainable parameters. Each aggregation round therefore involved exchanging a parameter vector of only that size. Local training used the Adam optimizer with a mini-batch size of 1,024 and a learning rate of 1 × 10⁻⁴, with 100 local epochs per update iteration across 20 update iterations, for a total of 2,000 local epochs per node in the federated setting.

Performance was measured with the C-statistic, a measure of how well a model discriminates between individuals who do and do not experience events, reported as the mean and 95% confidence interval across 10 independent runs. In the Rotterdam Study, the primary evaluation cohort because of its complete follow-up, the C-statistic rose from 0.723 (95% CI: 0.714–0.732) for the locally trained model to 0.737 (95% CI: 0.727–0.747) with federated training. In Lifelines, the increase was more modest, from 0.791 (95% CI: 0.783–0.799) to 0.795 (95% CI: 0.785–0.804). The improvement was larger in the Rotterdam Study, plausibly because its smaller sample size and lower baseline performance allowed it to benefit more from the information contributed by the vastly larger partner cohort.

The researchers were careful to rule out a tempting alternative explanation: that the federated model simply won by training longer. When the local Rotterdam Study model was trained for 2,000 epochs, matching the total local training budget of the federated setting, its mean C-statistic was 0.724 (95% CI: 0.709–0.739), essentially unchanged from the 100-epoch result. The gain therefore came from the other cohort’s data rather than from additional computation. The convergence trajectories also differed between the sites: the Rotterdam Study captured most of its improvement at the very first aggregation and then plateaued between 0.733 and 0.738, while Lifelines improved gradually and near-monotonically across all 20 iterations, consistent with the aggregated model being weighted heavily toward the Lifelines node.

Perhaps the most intriguing finding concerns data quality. A central worry in federated healthcare AI is that a large cohort with noisy outcomes could drag down a smaller one with precise data. Here, the opposite appeared to happen: despite Lifelines’ self-reported outcomes and interval-based event timing, its inclusion through federated learning was associated with higher performance on the Rotterdam Study, suggesting that the sheer volume of information in the larger cohort compensated, at least partially, for the uncertainty in its labels. The authors caution that unreported events in Lifelines would enter the partial likelihood as censored observations, potentially weakening the estimated associations, and that because FedAvg weights by sample size, any such weakened signal could propagate into the aggregated model in proportion to Lifelines’ dominant contribution. The observed improvement may therefore be a conservative estimate with respect to this source of uncertainty.

The study also comes with clearly stated boundaries. Privacy preservation here means keeping participant-level data within local institutional environments under the cohorts’ governance arrangements, including a Joint Controller Agreement and a Data Protection Impact Assessment, not a formal guarantee against inference attacks on the exchanged model parameters; techniques such as secure aggregation and differential privacy could further reduce those risks, though both may add computational overhead and, in differential privacy’s case, reduce performance. Only two nodes participated, both in the Netherlands, so generalizability to more institutions or to populations with different demographic and ethnic composition remains untested. Non-cardiovascular deaths were treated as censoring events rather than competing risks, which may overestimate absolute risk in the older Rotterdam Study participants, and the Cox partial likelihood’s reliance on risk sets means the federated optimization is not equivalent to fitting a single model on pooled data.

The practical implications are nonetheless striking. The entire federated training schedule ran in roughly 30 minutes on a single CPU core for a Lifelines-sized dataset and about 2 minutes for a Rotterdam Study-sized one, on standard virtual machines without dedicated accelerators, indicating that the real cost of this kind of collaboration lies in orchestrating tasks and governance rather than computation. Whether a discrimination gain of this magnitude would change clinical decisions around statin or blood pressure treatment thresholds would require reclassification analysis, decision-analytic measures such as net benefit, and external validation. But as a demonstration that heterogeneous, real-world cohorts can jointly train deep survival models under existing data protection frameworks, the study offers a template for the multi-institutional, privacy-preserving medical AI that regulators and patients alike have been waiting for, with future directions pointing toward imaging-derived biomarkers, longitudinal predictors, and aggregation methods that account for differences in cohort size and data quality.

Subject of Research: Federated deep learning for privacy-preserving cardiovascular disease risk prediction using the Lifelines and Rotterdam Study cohorts

Article Title: Federated deep learning for privacy-preserving cardiovascular disease risk prediction

Article References: Mo, H., Smits, D., Birhanu, M. A., Leening, M. J. G., Bos, D., van der Harst, P., & Bron, E. E. (2026). Federated deep learning for privacy-preserving cardiovascular disease risk prediction. Frontiers in Applied Mathematics and Statistics, 12, Article 1923866. https://doi.org/10.3389/fams.2026.1923866

Image Credits: AI Generated

DOI: 10.3389/fams.2026.1923866

Keywords: federated learning, cardiovascular disease, risk prediction, deep learning, DeepSurv, Lifelines, Rotterdam Study, privacy, FedAvg, C-statistic, Vantage6, survival analysis