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Updated: Jun 13, 2026

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

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Published on: September 22, 2020

Multi-Output Machine Learning for Prediction of Postoperative Outcomes After Cardiac Surgery Using Patient Blood

Henrique Coelho^1,2,3, Diana Paupério⁴, Fernando Silva^3,5

¹CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal.

Journal of Clinical Medicine

|June 12, 2026

Summary

This summary is machine-generated.

This study introduces an explainable multi-output model to predict multiple postoperative complications after adult cardiac surgery using clinical and patient blood management data. The model demonstrates feasibility for simultaneous outcome prediction, highlighting key predictive variables.

Keywords:

cardiac surgery machine learning multi-output regression patient blood management postoperative complications

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Published on: September 22, 2020

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Cardiac Surgery Outcomes Research

Background:

Postoperative complications in adult cardiac surgery are interconnected but often predicted by single-outcome models.
Routine clinical data and patient blood management (PBM) biomarkers offer potential for integrated prediction.
Existing models lack the ability to simultaneously predict multiple interrelated postoperative outcomes.

Purpose of the Study:

To develop and evaluate an explainable multi-output machine learning model for simultaneous prediction of multiple postoperative outcomes in adult cardiac surgery.
To integrate routinely collected clinical variables and PBM biomarkers into a predictive model.
To compare multi-output modeling with traditional mono-output approaches for specific endpoints.

Main Methods:

Retrospective analysis of 1414 adult cardiac surgery patients, with 513 complete cases analyzed.
Development of an explainable multi-output Decision Tree Regressor model.
Evaluation of model performance using R-squared, Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and Mean Absolute Percentage Error, alongside mono-output metrics like F1-scores.

Main Results:

The multi-output model achieved strong performance (R² = 0.83, MSE = 1.296).
Key predictors identified include creatinine, ferritin, platelet count, estimated glomerular filtration rate, preoperative red blood cell units, and EuroSCORE II.
High F1-scores were achieved for acute kidney injury (0.928), postoperative bleeding (0.970), infection (0.963), and 1-year hospital readmission (0.975).

Conclusions:

Explainable multi-output modeling is feasible for predicting postoperative outcomes in adult cardiac surgery using clinical and PBM data.
The developed model successfully integrates diverse variables to predict multiple outcomes simultaneously.
External validation is necessary before clinical implementation of this predictive model.