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Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and
Wei-Huan Hu1, Shang-Yi Lin2,3,4, Yuh-Jyh Hu1,5
1College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Background:
Clinical severity scores, such as acute physiology, age, chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), Pitt Bacteremia Score (PBS), and European Confederation of Medical Mycology Quality (EQUAL) score, may not reliably predict candidemia prognosis owing to their prespecified scorings that can limit their adaptability and applicability.
Objectives:
Unlike those fixed and prespecified scorings, we aim to develop and validate a machine learning (ML) approach that is able to learn predictive models adaptively from available patient data to increase adaptability and applicability.
Methods:
Different ML algorithms follow different design philosophies and consequently, they carry different learning biases. We have designed an ensemble meta-learner based on stacked generalisation to integrate multiple learners as a team to work at its best in a synergy to improve predictive performances.
Results:
In the multicenter retrospective study, we analysed 512 patients with candidemia from January 2014 to July 2019 and compared a stacked generalisation model (SGM) with APACHE II, SOFA, PBS and EQUAL score to predict the 14-day mortality. The cross-validation results showed that the SGM significantly outperformed APACHE II, SOFA, PBS, and EQUAL score across several metrics, including F1-score (0.68, p < .005), Matthews correlation coefficient (0.54, p < .05 vs. SOFA, p < .005 vs. the others) and the area under the curve (AUC; 0.87, p < .005). In addition, in an independent external test, the model effectively predicted patients' mortality in the external validation cohort, with an AUC of 0.77.
Conclusions:
ML models show potential for improving mortality prediction amongst patients with candidemia compared to clinical severity scores.
Insights
Machine learning models significantly improve mortality prediction for candidemia patients compared to traditional scores. This adaptive approach enhances applicability and performance in clinical settings.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Infectious Disease Epidemiology
Background:
- Traditional clinical severity scores (APACHE II, SOFA, PBS, EQUAL) have limitations in predicting candidemia prognosis.
- Prespecified scorings in existing models restrict adaptability and clinical applicability.
Purpose of the Study:
- To develop and validate a machine learning (ML) approach for adaptive prediction of candidemia prognosis.
- To enhance the adaptability and applicability of predictive models using patient data.
Main Methods:
- Designed an ensemble meta-learner using stacked generalization to integrate multiple ML algorithms.
- Compared a stacked generalization model (SGM) against established scores (APACHE II, SOFA, PBS, EQUAL) for 14-day mortality prediction.
Main Results:
- The SGM significantly outperformed traditional scores in predicting 14-day mortality across multiple metrics (F1-score, MCC, AUC).
- Cross-validation showed superior performance with an AUC of 0.87 (p < .005).
- An independent external validation cohort demonstrated effective mortality prediction with an AUC of 0.77.
Conclusions:
- Machine learning models demonstrate significant potential for improving mortality prediction in candidemia.
- The developed ML approach offers enhanced adaptability and applicability over fixed clinical scoring systems.
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