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Maximizing Survival in Pediatric Congenital Cardiac Surgery Using Machine Learning, Explainability, and Simulation
David Mauricio1, Jorge Cárdenas-Grandez1, Giuliana Vanessa Uribe Godoy2
1Department of Computer Science, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru.
Insights
This study introduces a novel method combining machine learning (ML), explainability techniques (ET), and simulation to improve outcomes in pediatric and congenital heart surgery (PCHS). The approach successfully reverses negative prognoses, enhancing patient survival rates.
Area of Science:
- Medical Informatics
- Surgical Outcomes Research
- Machine Learning in Healthcare
Background:
- Pediatric and congenital heart surgery (PCHS) carries significant risks, often stemming from disease severity or suboptimal timing of procedures.
- Existing prognostic models aid surgical decision-making but cannot actively reverse adverse outcomes.
- There is a critical need for advanced methods to improve survival probabilities in PCHS.
Purpose of the Study:
- To develop and validate an innovative approach integrating machine learning (ML), explainability techniques (ET), and simulation to reverse negative prognoses in PCHS.
- To enhance the accuracy of predicting surgical outcomes and identify key risk factors.
- To create a framework for designing personalized health scenarios to improve patient survival.
Main Methods:
- Utilized machine learning (ML) models for predicting mortality and survival in PCHS patients.
- Employed an explainability technique (ET), specifically LIME, to identify and quantify the impact of major risk factors.
- Integrated a simulation method to model potential health scenarios aimed at reversing negative prognoses.
Main Results:
- Achieved 96% accuracy in predicting mortality and survival using a dataset of 565 PCHS patients and 10 risk factors.
- Case studies confirmed LIME's explanations align with clinical observations.
- Successfully reversed an initial prognosis of death to survival in a simulated real-world case.
Conclusions:
- An integrated method combining ML, ET, and simulation effectively reverses negative prognoses in PCHS.
- The approach provides valuable insights for medical decision-making, supporting personalized patient care.
- Experimental validation demonstrates the potential to significantly improve outcomes in high-risk pediatric cardiac surgeries.
Abstract:
Background: Pediatric and congenital heart surgery (PCHS) is highly risky. Complications associated with this surgical procedure are mainly caused by the severity of the disease or the unnecessary, late, or premature execution of the procedure, which can be fatal. In this context, prognostic models are crucial to reduce the uncertainty of the decision to perform surgery; however, these models alone are insufficient to maximize the probability of success or to reverse a future scenario of patient death. Method: A new approach is proposed to reverse the prognosis of death in PCHS through the use of (1) machine learning (ML) models to predict the outcome of surgery; (2) an explainability technique (ET) to determine the impact of main risk factors; and (3) a simulation method to design health scenarios that potentially reverse a negative prognosis. Results: Accuracy levels of 96% in the prediction of mortality and survival were achieved using a dataset of 565 patients undergoing PCHS and assessing 10 risk factors. Three case studies confirmed that the ET known as LIME provides explanations that are consistent with the observed results, and the simulation of one real case managed to reverse the initial prognosis of death to one of survival. Conclusions: An innovative method that integrates ML models, ETs, and Simulation has been developed to reverse the prognosis of death in patients undergoing PCHS. The experimental results validate the relevance of this approach in medical decision-making, demonstrating its ability to reverse negative prognoses and provide a solid basis for more informed and personalized medical decisions.
