Federated Learning for Mortality Prediction in Intensive Care Units

Federated learning is a method to train a machine learning model on multiple remote datasets without the need to gather the data from the remote sites to a central location. In healthcare, gathering the data from different hospitals into a central location can be a difficult and time-consuming task, due to privacy concerns and regulations regarding the use of sensitive data, making federated learning an attractive alternative to more traditional methods. This thesis adapted an existing federated gradient boosting model and developed a new federated random forest model and applied them to mortality prediction in intensive care units. The results were then compared to the centralized counterparts of the models. The results showed that while the federated models did not perform as well as the centralized models on a similar sized dataset, the federated random forest model can achieve superior performance when trained on multiple hospitals' data compared to centralized models trained on a single hospital. In scenarios where the centralized models had data from multiple hospitals the federated models could not perform as well as the centralized models. It was also found that the performance of the centralized models could not be improved with further federated training. In addition to practical advantages such as possibility of parallel or asynchronous training without modifications to the algorithm, the federated random forest performed better in all scenarios compared to the federated gradient boosting. The performance of the federated random forest was also found to be more consistent over different scenarios than the performance of federated gradient boosting, which was highly dependent on factors such as the order with the hospitals were traversed

Federated Survival Forests

Survival 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

The Potential of an Enhanced Cooperation Measure in the EAFRD (2014-2020): the case of Ireland

This report was funded by the Department of Agriculture, Food and the Marine (DAFM) through the National Rural Network (February-May, 2012).The current Proposal for a Regulation of the European Parliament and of the Council on support for Rural Development by the European Agricultural Fund for Rural Development (EAFRD) includes Article (36) Cooperation that is potentially instrumental for realising the objectives of FOOD HARVEST 20204. The purpose of this report is to assess the scope and potential of Article 36 in the context of Irish agriculture and its findings have four key aspects. First, the main areas of confluence between Article 36 and primary policy objectives as set out in Food Harvest 2020 are identified. Second, a range of cooperation categories and types relevant to Article 36, many of which are operational in Ireland, are profiled. Third, drawing from case-studies of these co-operation types5, the operational characteristics of each type are presented, focusing on compatibility with Article 36. Possible supports that would encourage and assist the formation and operation of the cooperation types on a broad scale into the future, and also any possible constraints that would prevent success, are indicated. Fourth, a brief discussion of some key implementation considerations arising from the analysis overall is presented.Department of Agriculture, Food and the Marin

Even an infinite bureaucracy eventually makes a decision

We show that the fact that a political decision filtered through a finite tree of committees gives a determined answer generalises in some sense to infinite trees. This implies a new special case of the Matroid Intersection Conjecture