1,720,972 research outputs found
Federated Survival Analysis: Ensemble and Neural Methods for Distributed Time-to-Event Data
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Federated Survival Forests
Full text linkSurvival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data
Bridging the gap: improve neural survival models with interpolation techniques
Full text linkSurvival analysis is an essential tool in healthcare for risk assessment, assisting clinicians in their evaluation and decision making processes. Therefore, the importance of using expressive and high-performing survival models is crucial. With the advent of neural networks and deep learning, a new generation of survival models has emerged, offering state-of-the-art capabilities to capture the non-linear and complex relationships inherent in multimodal patient data for survival prediction. However, these models often produce discrete outputs, resulting in survival functions that are coarse-grained and difficult to interpret. This study advances previous research by further exploring interpolation techniques as a post-processing strategy to improve the predictive accuracy of survival models. Our results show how the use of specific interpolation techniques significantly improves the concordance and calibration of survival estimates. This analysis encompasses a wide array of medical datasets, models, and interpolation techniques, demonstrating the effectiveness of the proposed approach and providing actionable insights for survival model design
Deep Survival Analysis for Healthcare: An Empirical Study on Post-Processing Techniques
Full text linkSurvival analysis is a crucial tool in healthcare, allowing us to understand and predict time-to-event occurrences using statistical and machine-learning techniques. As deep learning gains traction in this domain, a specific challenge emerges: neural network-based survival models often produce discrete-time outputs, with the number of discretization points being much fewer than the unique time points in the dataset, leading to potentially inaccurate survival functions. To this end, our study explores post-processing techniques for survival functions. Specifically, interpolation and smoothing can act as effective regularization, enhancing performance metrics integrated over time, such as the Integrated Brier Score and the Cumulative Area-Under-the-Curve. We employed various regularization techniques on diverse real-world healthcare datasets to validate this claim. Empirical results suggest a significant performance improvement when using these post-processing techniques, underscoring their potential as a robust enhancement for neural network-based survival models. These findings suggest that integrating the strengths of neural networks with the non-discrete nature of survival tasks can yield more accurate and reliable survival predictions in clinical scenarios
Drug Inventory Control: Human Decisions Versus Deep Reinforcement Learning
Full text linkWe investigate whether and how deep reinforcement learning (DRL) can be exploited for managing inventory systems with a specific reference to perishable pharmaceutical products. A real-world case study is formulated as a Markov decision process, where states, actions, and rewards are defined. We then developed a DRL agent based on the Proximal Policy Optimization algorithm and compared its performance with a human decision-maker with several years of experience. Our findings reveal that the DRL agent outperforms the human policy by 11%, optimizing storage space and leading to growing profitability. Such incremental improvements can translate into substantial value for pharmaceutical companies operating in complex scenarios, and patients also stand to benefit. Finally, the study highlights the strategic advantage of integrating DRL into inventory management business operations, particularly for its ability to estimate uncertainty and manage corresponding supply chain risks
Latent Neural Cellular Automata for Resource-Efficient Image Restoration
No full textNeural cellular automata represent an evolution of the traditional cellular automata model, enhanced by the integration
of a deep learning-based transition function. This shift from a
manual to a data-driven approach significantly increases the
adaptability of these models, enabling their application in diverse domains, including content generation and artificial life.
However, their widespread application has been hampered by
significant computational requirements. In this work, we introduce the Latent Neural Cellular Automata (LNCA) model, a
novel architecture designed to address the resource limitations
of neural cellular automata. Our approach shifts the computation from the conventional input space to a specially designed
latent space, relying on a pre-trained autoencoder. We apply
our model in the context of image restoration, which aims to
reconstruct high-quality images from their degraded versions.
This modification not only reduces the model’s resource consumption but also maintains a flexible framework suitable for
various applications. Our model achieves a significant reduction in computational requirements while maintaining high
reconstruction fidelity. This increase in efficiency allows for
inputs up to 16 times larger than current state-of-the-art neural
cellular automata models, using the same resources
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
