Recent years have brought both a notable rise in the ability to efficiently harvest vast amounts of information, and a concurrent effort in preserving and actually enforcing the privacy of patients and their related data, as evidenced by the European GDPR. In these conditions, the Distributed Learning Ecosystem has shown great potential in allowing researchers to pool the huge amounts of sensitive data need to develop and validate prediction models in a privacy preserving way and with an eye towards personalized medicine. The aim of this abstract is to propose a privacy-preserving strategy for measuring the performance of Cox Proportional Hazard (PH) model
In the field of privacy-preserving data mining the common practice have been to gather data from the...
This thesis presents two contributions. The first contribution deals with the problem of siloed data...
The insights gained by the large-scale analysis of health-related data can have an enormous impact i...
Recent years have brought both a notable rise in theability to efficiently harvest vast amounts of i...
Artificial intelligence (AI) and automated decision-making have the potential to improve accuracy an...
The Cox proportional hazards model is one of the most popular survival analysis models used to deter...
Clinical time-to-event studies are dependent on large sample sizes, often not available at a single ...
Background: Artificial intelligence (AI) typically requires a significant amount of high-quality dat...
Privacy preservation plays a vital role in health care applications as the requirements for privacy ...
Background - Learning from routine healthcare data is important for the improvement of the quality o...
The predictive potential of the many large datasets being held in healthcare, financial markets, soc...
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to joi...
With the increasing number of data collectors such as smartphones, immense amounts of data are avail...
Distributed deep learning has potential for significant impact in preserving data privacy and improv...
Scientific collaborations benefit from sharing information and data from distributed sources, but pr...
In the field of privacy-preserving data mining the common practice have been to gather data from the...
This thesis presents two contributions. The first contribution deals with the problem of siloed data...
The insights gained by the large-scale analysis of health-related data can have an enormous impact i...
Recent years have brought both a notable rise in theability to efficiently harvest vast amounts of i...
Artificial intelligence (AI) and automated decision-making have the potential to improve accuracy an...
The Cox proportional hazards model is one of the most popular survival analysis models used to deter...
Clinical time-to-event studies are dependent on large sample sizes, often not available at a single ...
Background: Artificial intelligence (AI) typically requires a significant amount of high-quality dat...
Privacy preservation plays a vital role in health care applications as the requirements for privacy ...
Background - Learning from routine healthcare data is important for the improvement of the quality o...
The predictive potential of the many large datasets being held in healthcare, financial markets, soc...
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to joi...
With the increasing number of data collectors such as smartphones, immense amounts of data are avail...
Distributed deep learning has potential for significant impact in preserving data privacy and improv...
Scientific collaborations benefit from sharing information and data from distributed sources, but pr...
In the field of privacy-preserving data mining the common practice have been to gather data from the...
This thesis presents two contributions. The first contribution deals with the problem of siloed data...
The insights gained by the large-scale analysis of health-related data can have an enormous impact i...