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- Alabdallah, Abdallah, 1979-, et al.
(författare)
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The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models
- 2024
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Ingår i: Artificial Intelligence in Medicine. - Amsterdam : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 148
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Tidskriftsartikel (refereegranskat)abstract
- The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
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| 2. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Improving Concordance Index in Regression-based Survival Analysis : Discovery of Loss Function for Neural Networks
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this work, we use an Evolutionary Algorithm (EA) to discover a novel Neural Network (NN) regression-based survival loss function with the aim of improving the C-index performance. Our contribution is threefold; firstly, we propose an evolutionary meta-learning algorithm SAGA$_{loss}$ for optimizing a neural-network regression-based loss function that maximizes the C-index; our algorithm consistently discovers specialized loss functions that outperform MSCE. Secondly, based on our analysis of the evolutionary search results, we highlight a non-intuitive insight that signifies the importance of the non-zero gradient for the censored cases part of the loss function, a property that is shown to be useful in improving concordance. Finally, based on this insight, we propose MSCE$_{Sp}$, a novel survival regression loss function that can be used off-the-shelf and generally performs better than the Mean Squared Error for censored cases. We performed extensive experiments on 19 benchmark datasets to validate our findings.
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| 3. |
- Nyström, Axel, et al.
(författare)
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Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients
- 2024
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Ingår i: Journal of Electrocardiology. - Philadelphia, PA : Elsevier. - 0022-0736 .- 1532-8430. ; 82, s. 42-51
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Tidskriftsartikel (refereegranskat)abstract
- At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice. © 2023 The Authors
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