Privacy preserving federated learning for 90-day mortality prediction in colorectal surgery: a multicenter...

Limited data availability impedes the advancements of artificial intelligence (AI) applications in surgery to date. Federated learning (FL), a novel privacy-focused machine learning technique, introduces a decentralized framework to facilitate multicenter modeling. This study constructs a FL network, including differential privacy (DP), to predict 90-day mortality following colorectal surgery.

Patients undergoing elective colorectal surgery across three tertiary centers (C1-C3) between January 2015 and December 2021 were retrospectively enrolled. Neural networks (NN) for mortality prediction were trained and validated for all three centers individually and after data aggregation using centralized and distributed FL analysis. Local and central DP was then applied as additional data protection framework components. Areas under the receiver operating characteristic (AUROC) and precision-recallcurves (AUPRC) including 95%-confidence intervals were calculated.

A total of 2959 patients (mean [SD] age: 56.9 [16.8] years; n = 1677 [56.7%] male) were enrolled. The 90-day mortality rate was 3.1% (n = 92). Local NNs achieved AUROCs of 0.80 ([0.74-0.87]; C1), 0.81 ([0.75-0.87]; C2), and 0.84 ([0.75-0.92]; C3) and corresponding AUPRCs reaching 0.26 [0.11-0.41], 0.31 [0.21-0.41], and 0.21 [0.11-0.32], respectively. An aggregated centralized NN (NN CZ ) achieved an AUROC of 0.81 [0.76-0.85] and AUPRC of 0.29 [0.22-0.36]. Distributed FL (NN FL ) was comparable (AUROC: 0.78 [0.72-0.84], P = 0.67; AUPRC: 0.26 [0.17-0.35], P = 0.44) to the centralized model. Central DP reduced the performance of FL-based prediction by 5% in AUROC (0.74 [0.64-0.84]) and 35% in AUPRC (0.17 [0.10-0.23]). Local DP almost diminished the performance (AUROC: 0.52 [0.49-0.55]; AUPRC: 0.05 [0.03-0.06]). Feature importance analysis revealed age, blood status and the Charlson Comorbidity Index as highest weighted features for both NN CZ and NN FL .

Federated learning demonstrates similar performance to centralized machine learning in preoperative mortality prediction, providing an encouraging framework to accelerate the development of future surgical AI applications. Improving data privacy through DP is associated with compromises in model performance.